Meta-analysis of cellular toxicity for cadmium-containing quantum dots

Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance

Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly platforms, has proven beneficial for individuals with limited knowledge in ML-based predictive model development. These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity prediction models. AutoML platforms, namely Vertex AI, Azure, and Dataiku, were employed and performance measures such as accuracy, F1 score, precision, and recall for these autoML-based models were then compared with those of conventional ML-based models. The results demonstrated clearly that the autoML platforms produced more reliable nanotoxicity prediction models, outperforming those built with conventional ML algorithms. While none of the three autoML platforms significantly outperformed the others, distinctions exist among them in terms of the available options for choosing technical features throughout the model development steps. This allows users to select an autoML platform that aligns with their knowledge of predictive model development and its technical features. Additionally, prediction models constructed from datasets with better data quality displayed, enhanced performance than those built from datasets with lower data quality, indicating that future studies with high-quality datasets can further improve the performance of those autoML-based prediction models.

Ions and nanoparticles of Ag and/or Cd metals in a model aquatic microcosm: Effects on the abundance, diversity and functionality of the sediment bacteriome

Metals can be adsorbed on particulate matter, settle in sediments and cause alterations in aquatic environments. This study assesses the effect of Ag and/or Cd, both in ionic and nanoparticle (NP) forms, on the microbiome of sediments. For that purpose, aquatic controlled-microcosm experiments were exposed to an environmentally relevant and at tenfold higher doses of each form of the metals. Changes in the bacteriome were inferred by 16S rDNA sequencing. Ionic Ag caused a significant decrease of several bacterial families, whereas the effect was opposite when mixed with Cd, e.g., Desulfuromonadaceae family; in both cases, the bacteriome functionalities were greatly affected, particularly the nitrogen and sulfur metabolism. Compared to ionic forms, metallic NPs produced hardly any change in the abundance of microbial families, although the α-biodiversity of the bacteriome was reduced, and the functionality altered, when exposed to the NPs´ mixture. Our goal is to understand how metals, in different forms and combinations, released into the environment may endanger the health of aquatic ecosystems. This work may help to understand how aquatic metal pollution alters the structure and functionality of the microbiome and biogeochemical cycles, and how these changes can be addressed.

The utilization of quantum dot labeling as a burgeoning technique in the field of biological imaging

Quantum dots (QDs), with their unique optical and physical properties, have revolutionized the field of biological imaging, providing researchers with tools to explore cellular processes and molecular interactions in unprecedented detail. This review explores the diverse properties of QDs, emphasizing their application in biological imaging and addressing both their advantages and challenges. We discuss the developments in QD technology that have facilitated their integration into bioimaging, highlighting the role of surface modifications in enhancing their biocompatibility and functionality. The varied applications of QDs in both in vitro and in vivo imaging settings are examined, showcasing their capacity to deliver brighter, more stable, and multiplexed imaging solutions compared to traditional fluorescent dyes. Furthermore, we delve into the challenges associated with QD use, particularly concerns regarding their potential toxicity and long-term effects on biological systems, and explore ongoing research aimed at mitigating these issues. Finally, we discuss future directions in QD technology, anticipating advancements that will further solidify their role in biological imaging and open up new avenues for scientific exploration.

Changes in characteristics and risk of freshwater microplastics under global warming

Microplastics present a significant threat to freshwater ecosystems. However, the impact of global warming on their characteristics and associated risks remains uncertain. This study collected 2793 sample sites from literature and datasets to create a new risk assessment and rank methodology, known as the Multi-characteristics Potential Ecological Risk Index (MPERI), which incorporates various microplastic characteristics, such as concentration, size distribution, color, shape, and polymer diversity. Using regression random forest models (RRF), this study predicted that a 10 °C increase would raise microplastic concentration from 12,465.34 ± 68,603.87 to 13,387.17 ± 60,692.96 particles/m3. The percentage of small-size microplastics initially decreased (from 69.10 % to 68.72 %) and then increased (from 68.72 % to 68.78 %), while the diversity of color, shape, and polymer decreased by 0.29 %, 3.24 %, and 0.17 %, respectively. Furthermore, global warming could increase the rank of microplastic risks from high (405.25 ± 528.9) to dangerous (535.37 ± 582.03) based on the MPERI method. Most countries would experience an increase in risk values, with Indonesia and Vietnam transitioning from low to medium risk, and China and Malaysia transitioning from high to dangerous risk. The feature importance assessment of the RRF model indicated that concentration was the most influential variable in determining the change in risk values. While other microplastic characteristics had a lesser impact compared to concentration, they still influenced the risk ranking. This study highlights the role of global warming in shaping microplastic risks.

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.

Rational Design of NIR-II Emitting Conjugated Polymer Derived Nanoparticles for Image-Guided Cancer Interventions

Due to the reduced absorption, light scattering, and tissue autofluorescence in the NIR-II (1000-1700 nm) region, significant efforts are underway to explore diverse material platforms for in vivo fluorescence imaging, particularly for cancer diagnostics and image-guided interventions. Of the reported imaging agents, nanoparticles derived from conjugated polymers (CPNs) offer unique advantages to alternative materials including biocompatibility, remarkable absorption cross-sections, exceptional photostability, and tunable emission behavior independent of cell labeling functionalities. Herein, the current state of NIR-II emitting CPNs are summarized and structure-function-property relationships are highlighted that can be used to elevate the performance of next-generation CPNs. Methods for particle processing and incorporating cancer targeting modalities are discussed, as well as detailed characterization methods to improve interlaboratory comparisons of novel materials. Contemporary methods to specifically apply CPNs for cancer diagnostics and therapies are then highlighted. This review not only summarizes the current state of the field, but offers future directions and provides clarity to the advantages of CPNs over other classes of imaging agents.

Optimizing the conversion of phosphoenolpyruvate to lactate by enzymatic channeling with mixed nanoparticle display

Co-assembling enzymes with nanoparticles (NPs) into nanoclusters allows them to access channeling, a highly efficient form of multienzyme catalysis. Using pyruvate kinase (PykA) and lactate dehydrogenase (LDH) to convert phosphoenolpyruvic acid to lactic acid with semiconductor quantum dots (QDs) confirms how enzyme cluster formation dictates the rate of coupled catalytic flux (kflux) across a series of differentially sized/shaped QDs and 2D nanoplatelets (NPLs). Enzyme kinetics and coupled flux were used to demonstrate that by mixing different NP systems into clusters, a >10× improvement in kflux is observed relative to free enzymes, which is also ≥2× greater than enhancement on individual NPs. Cluster formation was characterized with gel electrophoresis and transmission electron microscopy (TEM) imaging. The generalizability of this mixed-NP approach to improving flux is confirmed by application to a seven-enzyme system. This represents a powerful approach for accessing channeling with almost any choice of enzymes constituting a multienzyme cascade.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS

Heavy metals raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment. However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well-trained multi-grained cascade forest models exhibited high accuracy (> 90%) and predictive performance (R2 ∼ 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations. This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would provide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.

CdSe/ZnS Quantum Dots' Impact on In Vitro Actin Dynamics

Quantum dots (QDs) are a novel type of nanomaterial that has unique optical and physical characteristics. As such, QDs are highly desired because of their potential to be used in both biomedical and industrial applications. However, the mass adoption of QDs usage has raised concerns among the scientific community regarding QDs' toxicity. Although many papers have reported the negative impact of QDs on a cellular level, the exact mechanism of the QDs' toxicity is still unclear. In this investigation, we study the adverse effects of QDs by focusing on one of the most important cellular processes: actin polymerization and depolymerization. Our results showed that QDs act in a biphasic manner where lower concentrations of QDs stimulate the polymerization of actin, while high concentrations of QDs inhibit actin polymerization. Furthermore, we found that QDs can bind to filamentous actin (F-actin) and cause bundling of the filament while also promoting actin depolymerization. Through this study, we found a novel mechanism in which QDs negatively influence cellular processes and exert toxicity.

Organic matters adsorbed on goethite inhibited the heterogeneous aggregation and adsorption of CdSe quantum dots: Experiments and extended DLVO theory

The widespread use of Cd-based quantum dots (Cd-QDs) has led to their inevitable release into the environment, and the prevalent iron oxides and natural organic matter (NOM) are the key factors affecting the environmental behavior and fate of Cd-QDs. However, the impact of NOM adsorbed on iron oxides on the behavior of Cd-QDs with iron oxides and the mechanism of its interaction are not clear. In this study, two kinds of water-soluble QDs (CdSe QDs and core-shell CdSe/ZnS QDs) were selected to study the aggregation and adsorption behavior on goethite (Goe) and goethite-humic acid/fulvic acid composites (Goe-HA/FA). Aggregation kinetics and adsorption experiments between QDs and Goe(-HA/FA), characterization, and extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory calculations indicated that electrostatic interaction was the dominant force for QDs adsorption on Goe(-HA/FA). HA/FA changed the surface charge of Goe and increased the electrostatic repulsion and steric hindrance between the particles, which in turn inhibited the adsorption of QDs on Goe. Besides, unsubstituted aromatic carbons, carboxy carbons, and carbonyl carbons played an important role in the adsorption process, and chemisorption occurred between QDs and Goe(-HA/FA). Our findings are important for better assessing the transport, fate, and potential environmental impacts and risks of Cd-QDs in iron-rich environments.

Lawsonia inermis-organically modified chitosan intercalated bentonite clay: A multifunctional nanotheranostic system for controlled drug delivery, sensing and cellular imaging

This study presents the development of organo-bentonites (OBs); a cost-effective drug delivery system holding both sensing and imaging capabilities. The OBs were synthesized using quaternary ammonium cations derived from chitosan, Lawsonia inermis, and pyrene/anthracene carboxaldehyde combinations through a three-step process: Mannich reaction, quaternization, and intercalation. Physicochemical characterization confirms the organic modification of bentonite. The OBs: NQPB and NQAB hold substantial ciprofloxacin (Cipro) loading capacities (71.51 % and 78.04 %, respectively) and exhibit pH-dependent release profiles, suggesting their potential use as drug delivery platforms. Cell viability evaluation by MTT and live-dead assays indicates favourable results. Both OBs demonstrate fluorescence within the 450-500 nm range, and they display concentration-dependent fluorescence quenching and enhancement for NQPB and NQAB, respectively, in the presence of tryptophan (Trp), making them suitable for its detection. Confocal analysis further enunciates the live intracellular fluorescence upon OB uptake. In summary, the intrinsically fluorescent mesoporous OBs synthesized from Lawsonia inermis and chitosan exhibit multifunctionality, including Cipro delivery, Trp sensing, and live cell imaging. Among the OBs, NQAB could be considered as a promising theranostic platform owing to its superior cytocompatibility (>80 %), appreciable fluorescence, and controlled release profile.

Urbanization influences dissolved organic matter characteristics but microbes affect greenhouse gas concentrations in lakes

Recognition and prediction of dissolved organic matter (DOM) properties and greenhouse gas (GHG) emissions is critical to understanding climate change and the fate of carbon in aquatic ecosystems, but related data is challenging to interpret due to covariance in multiple natural and anthropogenic variables with high spatial and temporal heterogeneity. Here, machine learning modeling combined with environmental analysis reveals that urbanization (e.g., population density and artificial surfaces) rather than geography determines DOM composition and properties in lakes. The structure of the bacterial community is the dominant factor determining GHG emissions from lakes. Urbanization increases DOM bioavailability and decreases the DOM degradation index (Ideg), increasing the potential for DOM conversion into inorganic carbon in lakes. The traditional fossil fuel-based path (SSP5) scenario increases carbon emission potential. Land conversion from water bodies into artificial surfaces causes organic carbon burial. It is predicted that increased urbanization will accelerate the carbon cycle in lake ecosystems in the future, which deserves attention in climate models and in the management of global warming.

Multienzymatic Cascades and Nanomaterial Scaffolding-A Potential Way Forward for the Efficient Biosynthesis of Novel Chemical Products

Synthetic biology is touted as the next industrial revolution as it promises access to greener biocatalytic syntheses to replace many industrial organic chemistries. Here, it is shown to what synthetic biology can offer in the form of multienzyme cascades for the synthesis of the most basic of new materials-chemicals, including especially designer chemical products and their analogs. Since achieving this is predicated on dramatically expanding the chemical space that enzymes access, such chemistry will probably be undertaken in cell-free or minimalist formats to overcome the inherent toxicity of non-natural substrates to living cells. Laying out relevant aspects that need to be considered in the design of multi-enzymatic cascades for these purposes is begun. Representative multienzymatic cascades are critically reviewed, which have been specifically developed for the synthesis of compounds that have either been made only by traditional organic synthesis along with those cascades utilized for novel compound syntheses. Lastly, an overview of strategies that look toward exploiting bio/nanomaterials for accessing channeling and other nanoscale materials phenomena in vitro to direct novel enzymatic biosynthesis and improve catalytic efficiency is provided. Finally, a perspective on what is needed for this field to develop in the short and long term is presented.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

Comparison of Toxicity and Cellular Uptake of CdSe/ZnS and Carbon Quantum Dots for Molecular Tracking Using Saccharomyces cerevisiae as a Fungal Model

Plant resource sharing mediated by mycorrhizal fungi has been a subject of recent debate, largely owing to the limitations of previously used isotopic tracking methods. Although CdSe/ZnS quantum dots (QDs) have been successfully used for in situ tracking of essential nutrients in plant-fungal systems, the Cd-containing QDs, due to the intrinsic toxic nature of Cd, are not a viable system for larger-scale in situ studies. We synthesized amino acid-based carbon quantum dots (CQDs; average hydrodynamic size 6 ± 3 nm, zeta potential -19 ± 12 mV) and compared their toxicity and uptake with commercial CdSe/ZnS QDs that we conjugated with the amino acid cysteine (Cys) (average hydrodynamic size 308 ± 150 nm, zeta potential -65 ± 4 mV) using yeast Saccharomyces cerevisiae as a proxy for mycorrhizal fungi. We showed that the CQDs readily entered yeast cells and were non-toxic up to 100 mg/L. While the Cys-conjugated CdSe/ZnS QDs were also not toxic to yeast cells up to 100 mg/L, they were not taken up into the cells but remained on the cell surfaces. These findings suggest that CQDs may be a suitable tool for molecular tracking in fungi (incl. mychorrhizal fungi) due to their ability to enter fungal cells.

Synthesis and hybridization of CuInS2 nanocrystals for emerging applications

Copper indium sulfide (CuInS2) is a ternary A(I)B(III)X(VI)2-type semiconductor featuring a direct bandgap with a high absorption coefficient. In attempts to explore their practical applications, nanoscale CuInS2 has been synthesized with crystal sizes down to the quantum confinement regime. The merits of CuInS2 nanocrystals (NCs) include wide emission tunability, a large Stokes shift, long decay time, and eco-friendliness, making them promising candidates in photoelectronics and photovoltaics. Over the past two decades, advances in wet-chemistry synthesis have achieved rational control over cation-anion reactivity during the preparation of colloidal CuInS2 NCs and post-synthesis cation exchange. The precise nano-synthesis coupled with a series of hybridization strategies has given birth to a library of CuInS2 NCs with highly customizable photophysical properties. This review article focuses on the recent development of CuInS2 NCs enabled by advanced synthetic and hybridization techniques. We show that the state-of-the-art CuInS2 NCs play significant roles in optoelectronic and biomedical applications.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

Application of Se-Met to CdTe QDs significantly reduces toxicity by modulating redox balance and inhibiting apoptosis

Cadmium tellurium quantum dots (CdTe QDs) as one of the most widely used QDs have been reported the toxicity and biosafety in recent years, little work has been done to reduce their toxicity however. Based on the mechanisms of toxicity of CdTe QDs on liver target organs such as oxidative stress and apoptosis previously reported by other researchers, we investigated the mechanism of action of trace element selenium (Se) to mitigate the hepatotoxicity of CdTe QDs. The experimental results showed that Se-Met at 40-140 μg L-1 could enhance the function of intracellular antioxidant defense system and the molecular structure of related antioxidant enzymes by reduce the production of ROS by 45%, protecting the activity of antioxidants and up-regulating the expression of selenoproteins with antioxidant functions, Gpx1 increase 225% and Gpx4 upregulated 47%. In addition, Se-Met could alleviate CdTe QDs-induced apoptosis by regulating two apoptosis-inducing factors, as intracellular caspase 3/9 expression levels were reduced by 70% and 87%, decreased Ca2+ concentration, and increased mitochondrial membrane potential measurements. Overall, this study indicates that Se-Met has a significant protective effect on the hepatotoxicity of CdTe QDs. Se-Met can be applied to the preparation of CdTe QDs to inhibit its toxicity and break the application limitation.

QD-based fluorescent nanosensors: Production methods, optoelectronic properties, and recent food applications

Food quality and safety are crucial public health concerns with global significance. In recent years, a series of fluorescence detection technologies have been widely used in the detection/monitoring of food quality and safety. Due to the advantages of wide detection range, high sensitivity, convenient and fast detection, and strong specificity, quantum dot (QD)-based fluorescent nanosensors have emerged as preferred candidates for food quality and safety analysis. In this comprehensive review, several common types of QD production methods are introduced, including colloidal synthesis, self-assembly, plasma synthesis, viral assembly, electrochemical assembly, and heavy-metal-free synthesis. The optoelectronic properties of QDs are described in detail at the electronic level, and the effect of food matrices on QDs was summarized. Recent advancements in the field of QD-based fluorescent nanosensors for trace level detection and monitoring of volatile components, heavy metal ions, food additives, pesticide residues, veterinary-drug residues, other chemical components, mycotoxins, foodborne pathogens, humidity, and temperature are also thoroughly summarized. Moreover, we discuss the limitations of the QD-based fluorescent nanosensors and present the challenges and future prospects for developing QD-based fluorescent nanosensors. As shown by numerous publications in the field, QD sensors have the advantages of strong anti-interference ability, convenient and quick operation, good linear response, and wide detection range. However, the reported assays are laboratory-focused and have not been industrialized and commercialized. Promising research needs to examine the potential applications of bionanotechnology in QD-based fluorescent nanosensors, and focus on the development of smart packaging films, labeled test strips, and portable kits-based sensors.

Development of a Novel, Ecologically Friendly Generation of pH-Responsive Alginate Nanosensors: Synthesis, Calibration, and Characterisation

Measurement of the intracellular pH is particularly crucial for the detection of numerous diseases, such as carcinomas, that are characterised by a low intracellular pH. Therefore, pH-responsive nanosensors have been developed by many researchers due to their ability to non-invasively detect minor changes in the pH of many biological systems without causing significant biological damage. However, the existing pH-sensitive nanosensors, such as the polyacrylamide, silica, and quantum dots-based nanosensors, require large quantities of organic solvents that could cause detrimental damage to the ecosystem. As a result, this research is aimed at developing a new generation of pH-responsive nanosensors comprising alginate natural polymers and pH-sensitive fluorophores using an organic, solvent-free, and ecologically friendly method. Herein, we successfully synthesised different models of pH-responsive alginate nanoparticles by varying the method of fluorophore conjugation. The synthesised pH nanosensors demonstrated a low MHD with a relatively acceptable PDI when using the lowest concentration of the cross-linker Ca+2 (1.25 mM). All the pH nanosensors showed negative zeta potential values, attributed to the free carboxylate groups surrounding the nanoparticles' surfaces, which support the colloidal stability of the nanosensors. The synthesised models of pH nanosensors displayed a high pH-responsiveness with various correlations between the pH measurements and the nanosensors' fluorescence signal. In summation, pH-responsive alginate nanosensors produced using organic, solvent-free, green technology could be harnessed as potential diagnostics for the intracellular and extracellular pH measurements of various biological systems.

Recent Breakthroughs in Using Quantum Dots for Cancer Imaging and Drug Delivery Purposes

Cancer is one of the leading causes of death worldwide. Because each person's cancer may be unique, diagnosing and treating cancer is challenging. Advances in nanomedicine have made it possible to detect tumors and quickly investigate tumor cells at a cellular level in contrast to prior diagnostic techniques. Quantum dots (QDs) are functional nanoparticles reported to be useful for diagnosis. QDs are semiconducting tiny nanocrystals, 2-10 nm in diameter, with exceptional and useful optoelectronic properties that can be tailored to sensitively report on their environment. This review highlights these exceptional semiconducting QDs and their properties and synthesis methods when used in cancer diagnostics. The conjugation of reporting or binding molecules to the QD surface is discussed. This review summarizes the most recent advances in using QDs for in vitro imaging, in vivo imaging, and targeted drug delivery platforms in cancer applications.

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches

Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure-activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms; that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99-0.89), Q2 values (0.64-0.77), and Q2F1 values (0.99-0.71). Thus, the present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.

Cellular uptake, intracellular behavior, and acute/sub-acute cytotoxicity of a PEG-modified quantum dot with promising in-vivo biomedical applications

Quantum Dots (QDs) modified with branched Polyethylene Glycol-amine (6- or 8-arm PEG-amine) coupled with methoxy PEG (mPEG) hold great promise for in vivo biomedical applications due to a long half-life in blood and negligible toxicity. However, the potential risks regarding their concomitant prolonged co-incubation with cardiovascular and blood cells remains inconclusive. In the present study, the feasible, effective and convenient proliferating-restricted cell line models representing the circulatory system were established to investigate the cellular internalization followed by intracellular outcomes and resulting acute/sub-acute cytotoxicity of the 6-arm PEG-amine/mPEG QDs. We found a dose-, time- and cell type-dependent cellular uptake of the 6-arm PEG-amine/mPEG QDs, which was ten-fold lower compared to the traditional linear PEG-modified counterpart. The QDs entered cells via multiple endocytic pathways and were mostly preserved in Golgi apparatus for at least one week instead of degradation in lysosomes, resulting in a minimal acute cytotoxicity, which is much lower than other types of PEG-modified QDs previously reported. However, a sub-acute cytotoxicity of QDs were observed several days post exposure using the concentrations eliciting no-significant acute cytotoxic effects, which was associated with elevated ROS generation caused by QDs remained inside cells. Finally, a non-cytotoxic concentration of the QDs was identified at the sub-acute cytotoxic level. Our study provided important information for clinical translation of branched PEG-amine/mPEG QDs by elucidating the QDs-cell interactions and toxicity mechanism using the proliferation-restricted cell models representing circulatory system. What's more, we emphasized the indispensability of sub-acute cytotoxic effects in the whole biosafety evaluation process of nanomaterials like QDs.

Isolation, cloning, and tissue distribution and functional analysis of ShP-glycoprotein in the freshwater crab Sinopotamon henanense exposed to Cd and Cd-QDs

P-glycoprotein (Pgp), a member of ATP binding cassette (ABC) transporter family, can extrude toxic substances out of cells by mediating multi-xenobiotic resistance (MXR) in aquatic organisms, however, its regulation and association with MXR are still unclear. In this work, the genetic information of Pgp in freshwater crab Sinopotamon henanense (ShPgp) was revealed for the first time. ShPgp with a total of 4488 bp was cloned and analyzed, which includes 4044 bp open reading frame, 353 bp 3' untranslated region, and 91 bp 5' untranslated region. The recombinant ShPGP were expressed in Saccharomyces cerevisiae and taken for SDS-PAGE and western blot analysis. ShPGP was widely expressed in the midgut, hepatopancreas, testis, ovary, gill, hemocytes, accessory gonad and myocardium of the crabs studied. The images of immunohistochemistry indicated that ShPgp was mainly distributed in the cytoplasm and cell membrane. When the crabs were exposed to cadmium or cadmium containing quantum dots (Cd-QDs), not only the relative expression of ShPgp mRNA and the protein produced were enhanced, but also the MXR activity and ATP contents. The relative expression of target genes related to energy metabolism, detoxification and apoptosis was also determined in the carbs exposed to Cd or Cd-QDs. The results showed that bcl-2 was significantly down-regulated, while other genes were up-regulated except PPAR (not affected). However, when the Shpgp in treated crabs was interfering by knockdown technique, their apoptosis and the expression of proteolytic enzyme genes and transcription factors MTF1 and HSF1 were also elevated, while the expression of apoptosis inhibiting and fat metabolism genes were compromised. Based on the observation, we concluded that MTF1 and HSF1 were involved in gene transcription regulation of mt and MXR, respectively, while PPAR had limited regulatory effect on those genes in S. henanense. NF-κB may play a negligible role in the process of apoptosis in testes induced by cadmium or Cd-QDs. However, the detail information regarding Pgp involvement in SOD or MT, and its association with apoptosis during xenobiotics insults remain to be explored.

Time-resolved absorption measurements quantify the competition of energy and electron transfer between quantum dots and cytochrome c

We applied transient absorption spectroscopy to study the early photodynamics in a system composed of CdTe quantum dots (QDs) and cytochrome c (Cyt c) protein. In the QDs and Cyt c mixtures, about 25 % of the excited QD electrons quickly relax (∼23 ps) to the ground state and roughly 75 % decay on slower time scale - mostly due to quenching by Cyt c. On the basis of the assumed model, we estimated the contribution of electron transfer and other mechanisms to this quenching. The primary quenching mechanism is probably energy transfer but electron transfer makes a significant contribution (∼8 %), resulting in photoreduction of Cyt c. The lifetime of one fraction of reduced Cyt c (35-90 %) is ∼ 1 ms and the lifetime of the remaining fraction was longer than the ∼ 50-ms time window of the experiment. We speculate that, in the former fraction, the back electron transfer from the reduced Cyt c to QDs occurs and the latter fraction of Cyt c is stably reduced.

Cadmium Sulfide Quantum Dots, Mitochondrial Function and Environmental Stress: A Mechanistic Reconstruction through In Vivo Cellular Approaches in Saccharomyces cerevisiae

Research on the effects of engineered nanomaterials (ENMs) on mitochondria, which represent one of the main actors in cell function, highlighted effects on ROS production, gametogenesis and organellar genome replication. Specifically, the mitochondrial effects of cadmium sulfide quantum dots (CdS QDs) exposure can be observed through the variation in enzymatic kinetics at the level of the respiratory chain and also by analyzing modifications of reagent and products in term of the bonds created and disrupted during the reactions through Fourier-transform infrared spectroscopy (FTIR). This study investigated both in intact cells and in isolated mitochondria to observe the response to CdS QDs treatment at the level of electron transport chain in the wild-type yeast Saccharomyces cerevisiae and in the deletion mutant Δtom5, whose function is implicated in nucleo-mitochondrial protein trafficking. The changes observed in wild type and Δtom5 strains in terms of an increase or decrease in enzymatic activity (ranging between 1 and 2 folds) also differed according to the genetic background of the strains and the respiratory chain functionality during the CdS QDs treatment performed. Results were confirmed by FTIR, where a clear difference between the QD effects in the wild type and in the mutant strain, Δtom5, was observed. The utilization of these genetic and biochemical approaches is instrumental to clarify the mitochondrial mechanisms implicated in response to these types of ENMs and to the stress response that follows the exposure.

Recent trends in macromolecule-conjugated hybrid quantum dots for cancer theranostic applications

Quantum dots (QDs) are small nanoparticles with semiconductor properties ranging from 2 to 10 nanometers comprising 10-50 atoms. The single wavelength excitation character of QDs makes it more significant, as it can excite multiple particles in a confined surface simultaneously by narrow emission. QDs are more photostable than traditional organic dyes; however, when injected into tissues, whole animals, or ionic solutions, there is a significant loss of fluorescence. HQD-based probes conjugated with cancer-specific ligands, antibodies, or peptides are used in clinical diagnosis. It is more precise and reliable than standard immunohistochemistry (IHC) at minimal protein expression levels. Advanced clinical studies use photodynamic therapy (PDT) with fluorescence imaging to effectively identify and treat cancer. Recent studies revealed that a combination of unique characteristics of QDs, including their fluorescence capacity and abnormal expression of miRNA in cancer cells, were used for the detection and monitoring progression of cancer. In this review, we have highlighted the unique properties of QDs and the theranostic behavior of various macromolecule-conjugated HQDs leading to cancer treatment.

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.

Carbon Dots: Opportunities and Challenges in Cancer Therapy

Recently, carbon dots (CDs) have been actively studied and reported for their various properties. In particular, the specific characteristics of carbon dots have been considered as a possible technique for cancer diagnosis and therapy. This is also a cutting-edge technology that offers fresh ideas for treating various disorders. Though carbon dots are still in their infancy and have not yet shown their value to society, their discovery has already resulted in some noteworthy advancements. The application of CDs indicates conversion in natural imaging. Photography using CDs has demonstrated extraordinary appropriateness in bio-imaging, the discovery of novel drugs, the delivery of targeted genes, bio-sensing, photodynamic therapy, and diagnosis. This review seeks to provide a comprehensive understanding of CDs, including their benefits, characteristics, applications, and mode of action. In this overview, many CD design strategies will be highlighted. In addition, we will discuss numerous studies on cytotoxic testing to demonstrate the safety of CDs. The current study will address the production method, mechanism, ongoing research, and application of CDs in cancer diagnosis and therapy.

Emerging ultrasmall luminescent nanoprobes for in vivo bioimaging

Photoluminescence (PL) imaging has become a fundamental tool in disease diagnosis, therapeutic evaluation, and surgical navigation applications. However, it remains a big challenge to engineer nanoprobes for high-efficiency in vivo imaging and clinical translation. Recent years have witnessed increasing research efforts devoted into engineering sub-10 nm ultrasmall nanoprobes for in vivo PL imaging, which offer the advantages of efficient body clearance, desired clinical translation potential, and high imaging signal-to-noise ratio. In this review, we present a comprehensive summary and contrastive discussion of emerging ultrasmall luminescent nanoprobes towards in vivo PL bioimaging of diseases. We first summarize size-dependent nano-bio interactions and imaging features, illustrating the unique attributes and advantages/disadvantages of ultrasmall nanoprobes differentiating them from molecular and large-sized probes. We also discuss general design methodologies and PL properties of emerging ultrasmall luminescent nanoprobes, which are established based on quantum dots, metal nanoclusters, lanthanide-doped nanoparticles, and silicon nanoparticles. Then, recent advances of ultrasmall luminescent nanoprobes are highlighted by surveying their latest in vivo PL imaging applications. Finally, we discuss existing challenges in this exciting field and propose some strategies to improve in vivo PL bioimaging and further propel their clinical applications.

Evaluating the Catalytic Efficiency of the Human Membrane-type 1 Matrix Metalloproteinase (MMP-14) Using AuNP-Peptide Conjugates

Interactions of plasmonic nanocolloids such as gold nanoparticles and nanorods with proximal dye emitters result in efficient quenching of the dye photoluminescence (PL). This has become a popular strategy for developing analytical biosensors relying on this quenching process for signal transduction. Here, we report on the use of stable PEGylated gold nanoparticles, covalently coupled to dye-labeled peptides, as sensitive optically addressable sensors for determining the catalytic efficiency of the human matrix metalloproteinase-14 (MMP-14), a cancer biomarker. We exploit real-time dye PL recovery triggered by MMP-14 hydrolysis of the AuNP-peptide-dye to extract quantitative analysis of the proteolysis kinetics. Sub-nanomolar limit of detections for MMP-14 has been achieved using our hybrid bioconjugates. In addition, we have used theoretical considerations within a diffusion-collision framework to derive enzyme substrate hydrolysis and inhibition kinetics equations, which allowed us to describe the complexity and irregularity of enzymatic proteolysis of nanosurface-immobilized peptide substrates. Our findings offer a great strategy for the development of highly sensitive and stable biosensors for cancer detection and imaging.

Meta-Analysis of Cytotoxicity Studies Using Machine Learning Models on Physical Properties of Plant Extract-Derived Silver Nanoparticles

Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules' mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R². The obtained high value of R² and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Red CdSe/ZnS QDs' Intracellular Trafficking and Its Impact on Yeast Polarization and Actin Filament

Quantum dots are nanoparticles (2-10 nm) that emit strong and tunable fluorescence. Quantum dots have been heavily used in high-demand commercialized products, research, and for medical purposes. Emerging concerns have demonstrated the negative impact of quantum dots on living cells; however, the intracellular trafficking of QDs in yeast cells and the effect of this interaction remains unclear. The primary goal of our research is to investigate the trafficking path of red cadmium selenide zinc sulfide quantum dots (CdSe/ZnS QDs) in Saccharomyces cerevisiae and the impact QDs have on yeast cellular dynamics. Using cells with GFP-tagged reference organelle markers and confocal microscopy, we were able to track the internalization of QDs. We found that QDs initially aggregate at the exterior of yeast cells, enter the cell using clathrin-receptor-mediated endocytosis, and distribute at the late Golgi/trans-Golgi network. We also found that the treatment of red CdSe/ZnS QDs resulted in growth rate reduction and loss of polarized growth in yeast cells. Our RNA sequence analysis revealed many altered genes. Particularly, we found an upregulation of DID2, which has previously been associated with cell cycle arrest when overexpressed, and a downregulation of APS2, a gene that codes for a subunit of AP2 protein important for the recruitment of proteins to clathrin-mediated endocytosis vesicle. Furthermore, CdSe/ZnS QDs treatment resulted in a slightly delayed endocytosis and altered the actin dynamics in yeast cells. We found that QDs caused an increased level of F-actin and a significant reduction in profilin protein expression. In addition, there was a significant elevation in the amount of coronin protein expressed, while the level of cofilin was unchanged. Altogether, this suggests that QDs favor the assembly of actin filaments. Overall, this study provides a novel toxicity mechanism of red CdSe/ZnS QDs on yeast actin dynamics and cellular processes, including endocytosis.

Nanomaterials biotransformation: In planta mechanisms of action

Research on engineered nanomaterials (ENMs) exposure has continued to expand rapidly, with a focus on uncovering the underlying mechanisms. The EU largely limits the number and the type of organisms that can be used for experimental testing through the 3R normative. There are different routes through which ENMs can enter the soil-plant system: this includes the agricultural application of sewage sludges, and the distribution of nano-enabled agrochemicals. However, a thorough understanding of the physiological and molecular implications of ENMs dispersion and chronic low-dose exposure remains elusive, thus requiring new evidence and a more mechanistic overview of pathways and major effectors involved in plants. Plants can offer a reliable alternative to conventional model systems to elucidate the concept of ENM biotransformation within tissues and organs, as a crucial step in understanding the mechanisms of ENM-organism interaction. To facilitate the understanding of the physico-chemical forms involved in plant response, synchrotron-based techniques have added new potential perspectives in studying the interactions between ENMs and biota. These techniques are providing new insights on the interactions between ENMs and biomolecules. The present review discusses the principal outcomes for ENMs after intake by plants, including possible routes of biotransformation which make their final fate less uncertain, and therefore require further investigation.

Green Synthesis of Carbon Nanoparticles (CNPs) from Biomass for Biomedical Applications

In this review, we summarize recent work on the "green synthesis" of carbon nanoparticles (CNPs) and their application with a focus on biomedical applications. Recent developments in the green synthesis of carbon nanoparticles, from renewable precursors and their application for environmental, energy-storage and medicinal applications are discussed. CNPs, especially carbon nanotubes (CNTs), carbon quantum dots (CQDs) and graphene, have demonstrated utility as high-density energy storage media, environmental remediation materials and in biomedical applications. Conventional fabrication of CNPs can entail the use of toxic catalysts; therefore, we discuss low-toxicity manufacturing as well as sustainable and environmentally friendly methodology with a focus on utilizing readily available biomass as the precursor for generating CNPs.

Self-assembling ferrimagnetic fluorescent micelles for bioimaging guided efficient magnetic hyperthermia therapy

Multifunctional magnet-fluorescent nanocomposites are widely applied in biomedical applications. Incorporating biocompatible quantum dots with highly ferrimagnetic magnetic nanoparticles into one nanoplatform for achieving efficient magnetic hyperthermia therapy (MHT) is very important. Herein, we reported an amphiphilic block copolymer with a flowable hydrophobic chain to encapsulate highly ferrimagnetic magnetic nanoparticles and ZnS/InP quantum dots via a facile self-assembly method. The obtained ferrimagnetic fluorescent micelle (FMFM) exhibited a uniform diameter of about 180 nm. In stark contrast, larger aggregation (400 nm in diameter) inevitably occurred using common poly(D,L-lactide) (PLA)-based amphiphilic block copolymer with a rigid hydrophobic chain, which was readily cleared by the reticuloendothelial system (RES). The flowable FMFM exhibited long-term colloidal stability within one month and desired fluorescent stability within 84 h. Benefiting from the high ferrimagnetism, the FMFM revealed excellent magnetic heating effect and magnetic resonance imaging capability. With accurate manipulation under an external magnetic field, FMFM realized in vitro enhanced fluorescence imaging sensitivity and accumulation efficiency at the tumor region, achieving in vitro and vivo improved MHT efficacy.

Reeling them in: Ph2PSiMe3 in the sequential formation of InP magic-sized clusters

Indium phosphide magic-sized clusters (MSCs) have been identified as a key step in the growth of InP quantum dots (QDs). However, the need for elevated temperatures to form QDs from MSCs has limited our understanding of this transformation. Herein, we utilize Ph₂PSiMe₃ to identify additional MSC intermediate species, which absorb from 365 nm to 490 nm. Excitingly, particle growth was carried out at 100 °C without the use of acidic surfactants. We show that despite being discrete, stable, and isolable sizes of MSCs, they form a reaction continuum, thus providing further insight into the growth mechanism of InP.

Plant synthetic biology innovations for biofuels and bioproducts

Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. This review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

A Nanomedicine Structure-Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of science-informed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure-activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

Predicting the cytotoxicity of nanomaterials through explainable, extreme gradient boosting

Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP toxicity depends on physical and chemical NP properties (e.g., size, charge and surface area) in ways and magnitudes that are still unknown. We assess the average as well as the individual importance of NP atomic descriptors, along with chemical properties and experimental conditions, in determining cytotoxicity endpoints for several nanomaterials. We employ a multicenter cytotoxicity nanomaterial database (12 different materials with first and second dimensions ranging between 2.70 and 81.2 nm and between 4.10 and 4048 nm, respectively). We develop a regressor model based on extreme gradient boosting with hyperparameter optimization. We employ Shapley additive explanations to obtain good cytotoxicity prediction performance. Model performances are quantified as statistically significant Spearman correlations between the true and predicted values, ranging from 0.5 to 0.7. Our results show that i) size in situ and surface areas larger than 200 nm and 50 m2/g, respectively, ii) primary particles smaller than 20 nm; iii) irregular (i.e., not spherical) shapes and iv) positive Z-potentials contribute the most to the prediction of NP cytotoxicity, especially if lactate dehydrogenase (LDH) assays are employed for short experimental times. These results were moderately stable across toxicity endpoints, although some degree of variability emerged across dose quantification methods, confirming the complexity of nano-bio interactions and the need for large, systematic experimental characterization to reach a safer-by-design approach.

Threats to Terrestrial Plants from Emerging Nanoplastics

Nanoplastics are ubiquitous in ecosystems and impact planetary health. However, our current understanding on the impacts of nanoplastics upon terrestrial plants is fragmented. The lack of systematic approaches to evaluating these impacts limits our ability to generalize from existing studies and perpetuates regulatory barriers. Here, we undertook a meta-analysis to quantify the overall strength of nanoplastic impacts upon terrestrial plants and developed a machine learning approach to predict adverse impacts and identify contributing features. We show that adverse impacts are primarily associated with toxicity metrics, followed by plant species, nanoplastic mass concentration and size, and exposure time and medium. These results highlight that the threats of nanoplastics depend on a diversity of reactions across molecular to ecosystem scales. These reactions are rooted in both the spatial and functional complexities of nanoplastics and, as such, are specific to both the plastic characteristics and environmental conditions. These findings demonstrate the utility of interrogating the diversity of toxicity data in the literature to update both risk assessments and evidence-based policy actions.

Quantum Dots Mediated Imaging and Phototherapy in Cancer Spheroid Models: State of the Art and Perspectives

Quantum Dots (QDs) are fluorescent nanoparticles known for their exceptional optical properties, i.e., high fluorescence emission, photostability, narrow emission spectrum, and broad excitation wavelength. These properties make QDs an exciting choice for bioimaging applications, notably in cancer imaging. Challenges lie in their ability to specifically label targeted cells. Numerous studies have been carried out with QDs coupled to various ligands like peptides, antibodies, aptamers, etc., to achieve efficient targeting. Most studies were conducted in vitro with two-dimensional cell monolayers (n = 8902) before evolving towards more sophisticated models. Three-dimensional multicellular tumor models better recapitulate in vivo conditions by mimicking cell-to-cell and cell-matrix interactions. To date, only few studies (n = 34) were conducted in 3D in vitro models such as spheroids, whereas these models could better represent QDs behavior in tumors compared to monolayers. Thus, the purpose of this review is to present a state of the art on the studies conducted with Quantum Dots on spheroid models for imaging and phototherapy purposes.

Quantum dots-labeled polymeric scaffolds for in vivo tracking of degradation and tissue formation

The inevitable gap between in vitro and in vivo degradation rate of biomaterials has been a challenging factor in the optimal designing of scaffold's degradation to be balanced with new tissue formation. To enable non-/minimum-invasive tracking of in vivo scaffold degradation, chemical modifications have been applied to label polymers with fluorescent dyes. However, the previous approaches may have limited expandability due to complicated synthesis processes. Here, we introduce a simple and efficient method to fluorescence labeling of polymeric scaffolds via blending with near-infrared (NIR) quantum dots (QDs), semiconductor nanocrystals with superior optical properties. QDs-labeled, 3D-printed PCL scaffolds showed promising efficiency and reliability in quantitative measurement of degradation using a custom-built fiber-optic imaging modality. Furthermore, QDs-PCL scaffolds showed neither cytotoxicity nor secondary labeling of adjacent cells. QDs-PCL scaffolds also supported the engineering of fibrous, cartilaginous, and osteogenic tissues from mesenchymal stem/progenitor cells (MSCs). In addition, QDs-PCL enabled a distinction between newly forming tissue and the remaining mass of scaffolds through multi-channel imaging. Thus, our findings suggest a simple and efficient QDs-labeling of PCL scaffolds and minimally invasive imaging modality that shows significant potential to enable in vivo tracking of scaffold degradation as well as new tissue formation.

Potential Development of N-Doped Carbon Dots and Metal-Oxide Carbon Dot Composites for Chemical and Biosensing

Among carbon-based nanomaterials, carbon dots (CDs) have received a surge of interest in recent years due to their attractive features such as tunable photoluminescence, cost effectiveness, nontoxic renewable resources, quick and direct reactions, chemical and superior water solubility, good cell-membrane permeability, and simple operation. CDs and their composites have a large potential for sensing contaminants present in physical systems such as water resources as well as biological systems. Tuning the properties of CDs is a very important subject. This review discusses in detail heteroatom doping (N-doped CDs, N-CDs) and the formation of metal-based CD nanocomposites using a combination of matrices, such as metals and metal oxides. The properties of N-CDs and metal-based CDs nanocomposites, their syntheses, and applications in both chemical sensing and biosensing are reviewed.

Importance of Metal Biotransformation in Cell Response to Metallic Nanoparticles: A Transcriptomic Meta-analysis Study

Metallic nanoparticles are increasingly present in our environment, raising concerns on their interactions with living organisms and potential toxicity. Indeed, metallic nanoparticles release metal ions that can be toxic, bioessential, therapeutically active, or combine several of these features. However, human cell responses to different metallic nanoparticles and ions have rarely been compared so far. We propose here a meta-analysis of the transcriptomic responses of human cells to nanoparticles and ions of various metals (titanium, iron, copper, zinc, silver, cadmium, platinum, gold), in order to identify the commonalities and differences between cell responses to these compounds. This analysis revealed that the chemical properties of metals are more important than their known biological functions (i.e., essential metals, toxicity) in governing the cell transcriptome. Particularly, we evidence that the response to nanoparticles is dominated by the response to the ions they contain, and depend on the nanoparticles' solubility. The formulation as nanoparticles impacts the cell response at lower intensity than the released ions, by altering genes related to vesicle intracellular transport and the cytoskeleton. Moreover, we put into light that several metals (i.e., copper, zinc, silver, cadmium, and gold) trigger a common cell response governed by metallothioneins, which coexist with singular signatures that are specific to a given element.