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

Comparative Analysis of Proteins Regulated during Cadmium Sulfide Quantum Dots Response in Arabidopsis thaliana Wild Type and Tolerant Mutants

In previous work, two independent Arabidopsis thaliana Ac/Ds transposon insertional mutant lines, atnp01 and atnp02, were identified that showed a higher level of tolerance than the wild type (wt) line to cadmium sulfide quantum dots (CdS QDs). The tolerance response was characterized at physiological, genetic and transcriptomic levels. In this work, a comparative analysis was performed on protein extracts from plantlets of the two mutants and of wt, each treated with 80 mg L^-1 CdS QDs. A comparative protein analysis was performed by 2D-PAGE, and proteins were characterized by MALDI-TOF/TOF mass spectrometry. Of 250 proteins identified from all three lines, 98 showed significant changes in relative abundance between control and CdS QD-treated plantlets. The wt, atnp01, and atnp02 control-treated pairs respectively showed 61, 31, and 31 proteins with differential expression. The two mutants had a different response to treatment in terms of type and quantity of up- and downregulated proteins. This difference became more striking when compared to wt. A network analysis of the proteins differentially expressed in atnp01 and atnp02 included several of those encoded by putative genes accommodating the transposons, which were responsible for regulation of some proteins identified in this study. These included nifu-like protein 3 (Nfu3), involved in chloroplast assembly, elongator complex 3 (Elo3), involved in transcriptional elongation, magnesium-chelate subunit-2 (Chli2), involved in chlorophyll biosynthesis, and protein phosphatase 2C (PP2C) which mediates abiotic stress response.

Reproductive toxicity of quantum dots on gonads of the fresh water crab Sinopotamon henanense

Since nano-quantum dots (QDs) are increasingly used as fluorescent dyes in biomedical sciences, the possibility of QDs contaminating aquatic environments is generally increasing. There is concern about potential toxicity of QDs. However, their risks in the aquatic environment are not entirely understood. In this study, the freshwater crab Sinopotamon henanense was exposed to cadmium telluride (CdTe) QDs by intraperitoneal injection to detect the reproductive toxicity of QDs (1/32, 1/16 and 1/4 LD₅₀; Crab was exposed for 1, 3, 5, and 7 days). After CdTe QD exposure, no significant effect was detected on the body weight and gonadosomatic index. Additionally, morphological observations showed tissue vacuolation in the testis, and inflammatory cell infiltration in the ovary. The submicroscopic structure showed that exposure to CdTe QDs damaged the organelles and cell structures of the gonads of S. henanense. Among the adverse effects, pathological changes in the nuclear membrane, mitochondria and lysosomes were particularly significant. Antioxidant enzymes responded differently to different doses of QDs. The 0.5-mg/kg dose induced superoxide dismutase activity in the testes. And in the 1-mg/kg and 4-mg/kg dose QD exposure test, the testis responded by activating glutathione peroxidase and inducing reduced glutathione and overconsuming glutathione peroxidase. Respectively, the ovaries responded by overconsuming superoxide dismutase and glutathione peroxidase and reduced glutathione. Thus, we conclude that the gonads of S. henanense were injured by CdTe QD, and male are better indicators of the toxicity of QDs than female crabs according to greater alterations in tissue structure and antioxidant enzyme in the analyses.

Biosynthesized Quantum Dots as Improved Biocompatible Tools for Biomedical Applications

Quantum Dots (QDs), whose diameters are often limited to 10 nm, have been of interest to researchers for their unique optical characteristics, which are attributed to quantum confinement. Following their early application in the electrical industry as light-emitting diode materials, semiconductor nanocrystals have continued to show great potential in clinical diagnosis and biomedical applications. The conventional physical and chemical pathways for QD syntheses typically require harsh conditions and hazardous reagents, and these products encounter non-hydrophilic problems due to organic capping ligands when they enter the physiological environment. The natural reducing abilities of living organisms, especially microbes, are then exploited to prepare QDs from available metal precursors. Low-cost and eco-friendly biosynthesis approaches have the potential for further biomedical applications which benefit from the good biocompatibility of protein-coated QDs. The surface biomass offers many binding sites to modify substances or target ligands, therefore achieving multiple functions through simple and efficient operations. Biosynthetic QDs could function as bioimaging and biolabeling agents because of their luminescence properties similar to those of chemical QDs. In addition, extensive research has been carried out on the antibacterial activity, metal ion detection and bioremediation. As a result, this review details the advanced progress of biomedical applications of biosynthesized QDs and illustrates these principles as clearly as possible.

Nanotheranostics through Mitochondria-targeted Delivery with Fluorescent Peptidomimetic Nanohybrids for Apoptosis Induction of Brain Cancer Cells

Overview: Malignant brain tumors remain one of the greatest challenges faced by health professionals and scientists among the utmost lethal forms of cancer. Nanotheranostics can play a pivotal role in developing revolutionary nanoarchitectures with multifunctional and multimodal capabilities to fight cancer. Mitochondria are vital organelles to eukaryotic cells, which have been recognized as a significant target in cancer therapy where, by damaging the mitochondria, it will cause irreparable cell death or apoptosis. Methods: We designed and produced novel hybrid nanostructures comprising a fluorescent semiconductor core (AgInS2, AIS) and cysteine-modified carboxymethylcellulose (termed thiomer, CMC_Cys) conjugated with mitochondria-targeting peptides (KLA) forming a macromolecular shell for combining bioimaging and for inducing brain cancer cell (U-87 MG) death. Results: The optical and physicochemical properties of the nanoconjugates demonstrated suitability as photoluminescent nanostructures for cell bioimaging and intracellular tracking. Additionally, the results proved a remarkable killing activity towards glioblastoma cells of cysteine-bearing CMC conjugates coupled with KLA peptides through the half-maximal effective concentration values, approximately 70-fold higher compared to the conjugate analogs without Cys residues. Moreover, these thiomer-based pro-apoptotic drug nanoconjugates displayed higher lethality against U-87 MG cancer cells than doxorubicin, a model drug in chemotherapy, although extremely toxic. Remarkably, these peptidomimetic nanohybrids demonstrated a relative "protective effect" regarding healthy cells while maintaining high killing activity towards malignant brain cells. Conclusion: These findings pave the way for developing hybrid nanoarchitectures applied as targeted multifunctional platforms for simultaneous imaging and therapy against cancer while minimizing the high systemic toxicity and side-effects of conventional drugs in anticancer chemotherapy.

Combination of the NanoSuit method and gold/platinum particle-based lateral flow assay for quantitative and highly sensitive diagnosis using a desktop scanning electron microscope

Owing to its simplicity and low cost, the lateral flow assay (LFA) is one of the most commonly used point-of-care diagnostic techniques, despite its low sensitivity and poor quantification. Here, we report a newly developed LFA-NanoSuit method (LNSM) combined with a desktop scanning electron microscope (SEM) for the direct observation of immunocomplexes labeled with a colloidal metal instead of signal enhancement strategies, such as using color, electrochemical signals, silver enhancement, magnetic properties, luminescent, and surface-enhanced Raman spectroscopy (SERS). The proposed LNSM suppresses cellulose deformity, thereby allowing the acquisition of high-resolution images of gold/platinum-labeled immunocomplexed pathogens such as influenza A, without conductive treatment as in conventional SEM. Electron microscopy-based diagnosis of influenza A exhibited 94 % clinical sensitivity (29/31; 95 % confidence interval [CI]: 79.3-98.2 %) and 100 % clinical specificity (95 % CI: 98.1-100 %), which was more sensitive (71.4 %) than visual detection (14.3 %), especially in the lower influenza A-RNA copy number group. The detection ability of our method was nearly comparable to that of real-time reverse transcription-PCR. This is the first report on the diagnosis of clinical diseases using LFA equipped with a desktop SEM. This simple and highly sensitive quantitative analysis method involving LFA can be used to diagnose various diseases in humans and livestock, including highly infectious diseases such as COVID-19.

Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling

The assessment of cytotoxicity of quantum dots is very essential for environmental and health risk analysis. In the present work we have modelled HeLa cell cytotoxicity of sixty one CdSe quantum dots with ZnS shell as a function of its experimental conditions and molecular construction using quasiSMILES representations. The index of ideality of correlation helps in the building of ten statistically significant models having good fitting ability with value of R² ranging from 0.8414 to 0.9609 for the training set. The split 5 model is rated as the best model with values of R², Q²_F1, Q²_F2 and Q²_F3 as 0.8964, 0.8267, 0.8264 and 0.8777 respectively for the calibration set. The extraction of features causing increase and decrease of cytotoxicity of quantum dots indicates importance of neutral surface charge, surface modified with protein, 72 h exposure time, combination of MTT assay with surface protein in decreasing the cytotoxicity. Amphiphilic polymer, polyol ligand with neutral charge, 0.5 - 0.6 nm quantum dot diameter with lipid ligand and unmodified positively charged surface are grouped in toxicity enhancer features. Further, consensus modelling using split 5 and 8 patterns enhances the prediction quality by increasing the R²_val to 0.9361 and 0.9656 respectively.

Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

Lab-on-Microsphere-FRET-Based Multiplex Sensor Platform

Here we report on the development and investigation of a novel multiplex assay model based on polymer microspheres (PMS) encoded with ternary AIS/ZnS quantum dots (QDs). The system was prepared via layer-by-layer deposition technique. Our studies of Förster resonance energy transfer (FRET) between the QD-encoded microspheres and two different cyanine dyes have demonstrated that the QD photoluminescence (PL) quenching steadily increases with a decrease in the QD-dye distance. We have found that the sensitized dye PL intensity demonstrates a clear maximum at two double layers of polyelectrolytes between QDs and Dye molecules on the polymer microspheres. Time resolved PL measurements have shown that the PL lifetime decreases for the QDs and increases for the dyes due to FRET. The designed system makes it possible to record spectrally different bands of FRET-induced dye luminescence with different decay times and thereby allows for the multiplexing by wavelength and photoluminescence lifetimes of the dyes. We believe that PMS encoded with AIS/ZnS QDs have great potential for the development of new highly selective and sensitive sensor systems for multiplex analysis to detect cell lysates and body fluids' representative biomarkers.

The video-rate imaging of sub-10 nm plasmonic nanoparticles in a cellular medium free of background scattering

Plasmonic nanoparticles (e.g., gold, silver) have attracted much attention for biological sensing and imaging as promising nanoprobes. Practical biomedical applications demand small gold nanoparticles (Au NPs) with a comparable size to quantum dots and fluorescent proteins. Very small nanoparticles with a size below the Rayleigh limit (usually <30-40 nm) are hard to see by light scattering using a dark-field microscope, especially within a cellular medium. A photothermal microscope is able to detect very small nanoparticles, down to a few nanometers, but the imaging speed is usually too slow (minutes to hours) to image living cell processes. Here an absorption modulated scattering microscopy (AMSM) method is presented, which allows for the imaging of sub-10 nm Au NPs within a cellular medium. The unique physical mechanism of AMSM offers the remarkable ability to remove the light scattering background of the cellular component. In addition to having a sensitivity comparable to that of photothermal microscopy, AMSM has a much higher imaging speed, close to the video rate (20 fps), which allows for the dynamic tracking of small nanoparticles in living cells. This AMSM method might be a valuable tool for living cell imaging, using sub-10 nm Au NPs as biological probes, and thereby unlocking many new applications, such as single molecule labeling and the dynamic tracking of molecular interactions.

Enhancement of tumor lethality of ROS in photodynamic therapy

In the process of photodynamic therapy (PDT) treatment of tumors, reactive oxygen species (ROS) plays a key role in destroying tumor tissues. However, traditional PDT often has limited ROS killing capacity due to hypoxia in the tumor microenvironment (TME) or obstruction by the ROS defense system, resulting in poor efficacy. Therefore, enhancing the killing effect of ROS on tumors is the core of enhancing the anti-tumor effect of PDT. In recent years, many studies have developed a series of strategies to enhance the ability of ROS to kill tumors in view of the limitations of the TME on PDT. This article summarizes the commonly used or innovative strategies in recent years, including not only frequently used methods for hypoxia in the TME but also innovative strategies to inhibit the ROS defense system.

Use of size-dependent electron configuration fingerprint to develop general prediction models for nanomaterials

Due to the lack of nano descriptors that can appropriately represent the wide chemical space of engineered nanomaterials (ENMs), applicability domain of nano-quantitative structure-activity relationship models are limited to certain types of ENMs, such as metal oxides, metals, carbon-based nanomaterials, or quantum dots. In this study, a size-dependent electron configuration fingerprint (SDEC FP) was introduced to estimate the quantity of electrons based on the core, doping, and coating materials of ENMs in different sizes. SDEC FP was used in prediction model development and nanostructure similarity analysis on datasets including metal and carbon-based nanomaterials with and without surface modifications. Cytotoxicity and zeta potential prediction models developed with SDEC FP achieved good prediction accuracies on test set. Nanostructure similarity analysis was performed through principal component analysis which showed that structural similarity between ENMs measured by SDEC FP was highly correlated with their properties.

Quantum dots in cell imaging and their safety issues

When quantum dots are used as fluorescent probes or drug tracers for in vivo imaging, the quantum dots in the blood will come into direct contact with vascular endothelial cells. Thus, it is necessary to study whether quantum dots can affect endothelial function after being injected into blood vessels as imaging agents. In recent years, there have been numerous studies on the toxicity of quantum dots. Herein, we focused on five types of quantum dots (Cd-containing quantum dots, CuInS2 quantum dots, black phosphorus quantum dots, MXene quantum dots, and carbon-based quantum dots) for cell imaging and their toxicity in vivo and in vitro. Although current research on the toxicity of quantum dots has not reached a consistent conclusion, it can guide the next step in evaluating their cytotoxicity.

Toxicity of Carbon Nanotubes: Molecular Mechanisms, Signaling Cascades, and Remedies in Biomedical Applications

Carbon nanotubes (CNTs) are the most studied allotropic form of carbon. They can be used in various biomedical applications due to their novel physicochemical properties. In particular, the small size of CNTs, with a large surface area per unit volume, has a considerable impact on their toxicity. Despite of the use of CNTs in various applications, toxicity is a big problem that requires more research. In this Review, we discuss the toxicity of CNTs and the associated mechanisms. Physicochemical factors, such as metal impurities, length, size, solubilizing agents, CNTs functionalization, and agglomeration, that may lead to oxidative stress, toxic signaling pathways, and potential ways to control these mechanisms are also discussed. Moreover, with the latest mechanistic evidence described in this Review, we expect to give new insights into CNTs' toxicological effects at the molecular level and provide new clues for the mitigation of harmful effects emerging from exposure to CNTs.

A Systematic Comparative Study of the Toxicity of Semiconductor and Graphitic Carbon-Based Quantum Dots Using In Vitro Cell Models

A comparative, fully parallel study of nanoparticles (NPs) toxicity by in vitro cell viability is shown looking for reliable comparability of nanotoxicological results, a well-recognized bottleneck in the context. This procedure is suitable to compare toxicity of similar NPs, as well as the influence on toxicity of the size, surface, and other characteristics. As a case of study, semiconductor (SQDs) and graphitic-carbon quantum dots (CQDs) with identical surface groups and size were evaluated. All experiments were conducted at same conditions, involving two types of cells (mouse fibroblasts (3T3-L1) and carcinoma human hepatocellular cells (HepG2)) and different extracellular components (in the absence or presence of fetal bovine serum (FBS)). Cell viability demonstrated the excellent biocompatibility of CQDs compared to SQDs, which caused higher percentage of cell death at lower concentrations, as predicted but never clearly demonstrated. However, our comparative studies established that the toxicity of SQDs and CQDs are cellular type-dependent, and the absence or presence of serum proteins reduces the minimal concentration necessary of NPs to produce toxicity.

Predicting nanotoxicity by an integrated machine learning and metabolomics approach

Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs.

Toxicity of different types of quantum dots to mammalian cells in vitro: An update review

Currently, there are a great quantity type of quantum dots (QDs) that has been developed by researchers. Depending on the core material, they can be roughly divided into cadmium, silver, indium, carbon and silicon QDs. And studies on the toxicity of QDs are also increasing rapidly, but in vivo tests in model animals fail to reach a consistent conclusion. Therefore, we review the literatures dealing with the cytotoxicity of QDs in mammalian cells in vitro. After a short summary of the application characteristics of five types of QDs, the fate of QDs in cells will be discussed, ranging from the uptake, transportation, sublocation and excretion. A substantial part of the review will be focused on in vitro toxicity, in which the type of QDs is combined with their adverse effect and toxic mechanism. Because of their different luminescent properties, different subcellular fate, and different degree of cytotoxicity, we provide an overview on the balance of optical stability and biocompatibility of QDs and give a short outlook on future direction of cytotoxicology of QDs.

Recent advances in nanoscale materials for antibody-based cancer theranostics

The quest for advanced management tools or options of various cancers has been on the rise to efficiently reduce their risks of mortality without the demerits of conventional treatments (e.g., undesirable side effects of the medications on non-target tissues, non-targeted distribution, slow clearance of the administered drugs, and the development of drug resistance over the duration of therapy). In this context, nanomaterials-antibody conjugates can offer numerous advantages in the development of cancer theranostics over conventional delivery systems (e.g., highly specific and enhanced biodistribution of the drug in targeted tissues, prolonged systemic circulation, low toxicity, and minimally invasive molecular imaging). This review comprehensively discusses and evaluates recent advances in the application of nanomaterial-antibody bioconjugates for cancer theranostics for the further advancement in the control of diverse cancerous diseases. Further, discussion is expanded to cover the various challenges and limitations associated with the design and development of nanomaterial-antibody conjugates applicable towards better management of cancer.

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

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

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base

In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the Daphnia magna model and assessing acute toxicity determined as immobilisation. Cadmium (Cd2+) and bovine serum albumin (BSA) were selected as co-pollutant and protein model system, respectively. Albumin corona formation on GO dramatically increased its colloidal stability (ca. 60%) and Cd2+ adsorption capacity (ca. 4.5 times) in reconstituted water (Daphnia medium). The acute toxicity values (48 h-EC50) observed were 0.18 mg L-1 for Cd2+-only and 0.29 and 0.61 mg L-1 following co-exposure of Cd2+ with GO and BSA@GO materials, respectively, at a fixed non-toxic concentration of 1.0 mg L-1. After coronation of GO with BSA, a reduction in cadmium toxicity of 110 % and 238% was achieved when compared to bare GO and Cd2+-only, respectively. Integration of datasets associated with graphene-based materials, heavy metals and mixture toxicity is essential to enable re-use of the data and facilitate nanoinformatics approaches for design of safer nanomaterials for water quality monitoring and remediation technologies. Hence, all data from this work were annotated and integrated into the NanoCommons Knowledge Base, connecting the experimental data to nanoinformatics platforms under the FAIR data principles and making them interoperable with similar datasets.

Selective and sensitive fluorescent nanoprobe based on AgInS2-ZnS quantum dots for the rapid detection of Cr (III) ions in the midst of interfering ions

We herein report a novel eco-friendly method for the fluorescent sensing of Cr (III) ions using green synthesized glutathione (GSH) capped water soluble AgInS₂-ZnS (AIS-ZnS) quantum dots (QDs). The as-synthesized AIS-ZnS QDs were speherical in shape with average diameter of ∼2.9 nm and exhibited bright yellow emission. The fluorimetric analyses showed that, compared to Cr (VI) ions and other 20 metal ions across the periodic table, AIS-ZnS QDs selectively detected Cr (III) ions via fluorescent quenching. In addition, AIS-ZnS QDs fluorescent nanoprobes exhibited selective detection of Cr (III) ions in the mixture of interfering divalent metal ions such as Cu (II), Pb (II), Hg (II), Ni (II). The mechanism of Cr (III) sensing investigated using HRTEM and FTIR revealed that the binding of Cr (III) ions with the GSH capping group resulted in the aggregation of QDs followed by fluorescence quenching. The limit of detection of Cr (III) ions was calculated to be 0.51 nM. The present method uses cadmium free QDs and paves a greener way for selective determination of Cr (III) ions in the midst of other ions in aqueous solutions.

Microalgal ecotoxicity of nanoparticles: An updated review

Nowadays, nanotechnology and its related industries are becoming a rapidly explosive industry that offers many benefits to human life. However, along with the increased production and use of nanoparticles (NPs), their presence in the environment creates a high risk of increasing toxic effects on aquatic organisms. Therefore, a large number of studies focusing on the toxicity of these NPs to the aquatic organisms are carried out which used algal species as a common biological model. In this review, the influences of the physio-chemical properties of NPs and the response mechanisms of the algae on the toxicity of the NPs were discussed focusing on the "assay" studies. Besides, the specific algal toxicities of each type of NPs along with the NP-induced changes in algal cells of these NPs are also assessed. Almost all commonly-used NPs exhibit algal toxicity. Although the algae have similarities in the symptoms under NP exposure, the sensitivity and variability of each algae species to the inherent properties of each NPs are quite different. They depend strongly on the concentration, size, characteristics of NPs, and biochemical nature of algae. Through the assessment, the review identifies several gaps that need to be further studied to make an explicit understanding. The findings in the majority of studies are mostly in laboratory conditions and there are still uncertainties and contradictory/inconsistent results about the behavioral effects of NPs under field conditions. Besides, there remains unsureness about NP-uptake pathways of microalgae. Finally, the toxicity mechanisms of NPs need to be thoughtfully understood which is essential in risk assessment.

Safety Considerations of Cancer Nanomedicine-A Key Step toward Translation

The rate of translational effort of nanomedicine requires strategic planning of nanosafety research in order to enable clinical trials and safe use of nanomedicine in patients. Herein, the experiences that have emerged based on the safety data of classic liposomal formulations in the space of oncology are discussed, along with a description of the new challenges that need to be addressed according to the rapid expansion of nanomedicine platform beyond liposomes. It is valuable to consider the combined use of predictive toxicological assessment supported by deliberate investigation on aspects such as absorption, distribution, metabolism, and excretion (ADME) and toxicokinetic profiles, the risk that may be introduced during nanomanufacture, unique nanomaterials properties, and nonobvious nanosafety endpoints, for example. These efforts will allow the generation of investigational new drug-enabling safety data that can be incorporated into a rational infrastructure for regulatory decision-making. Since the safety assessment relates to nanomaterials, the investigation should cover the important physicochemical properties of the material that may lead to hazards when the nanomedicine product is utilized in humans.

Nanoparticle Toxicology

Nanoparticles from natural and anthropogenic sources are abundant in the environment, thus human exposure to nanoparticles is inevitable. Due to this constant exposure, it is critically important to understand the potential acute and chronic adverse effects that nanoparticles may cause to humans. In this review, we explore and highlight the current state of nanotoxicology research with a focus on mechanistic understanding of nanoparticle toxicity at organ, tissue, cell, and biomolecular levels. We discuss nanotoxicity mechanisms, including generation of reactive oxygen species, nanoparticle disintegration, modulation of cell signaling pathways, protein corona formation, and poly(ethylene glycol)-mediated immunogenicity. We conclude with a perspective on potential approaches to advance current understanding of nanoparticle toxicity. Such improved understanding may lead to mitigation strategies that could enable safe application of nanoparticles in humans. Advances in nanotoxicity research will ultimately inform efforts to establish standardized regulatory frameworks with the goal of fully exploiting the potential of nanotechnology while minimizing harm to humans.

Recent Nanocarrier Approaches for Targeted Drug Delivery in Cancer Therapy

Cancer is one of the most serious health concerns in the 21st century whose prevalence is beyond boundaries and can affect any organ of the human body. The conventional chemotherapeutic treatment strategies lack specificity to tumors and are associated with toxic effects on the immune system and other organ systems. In the past decades, there has been continuous progress in the development of smart nanocarrier systems for target-specific delivery of drugs against a variety of tumors, including intracellular gene-specific targeting. These nanocarriers are able to recognize the tumor cells and deliver the therapeutic agent in fixed proportions, causing no or very less harm to healthy cells. Nanosystems have modified physicochemical properties, improved bioavailability, and long retention in blood, which enhances their potency. A huge number of nanocarrier based formulations have been developed and are in clinical trials. Nanocarrier systems include polymeric micelles, liposomes, dendrimers, carbon nanotubes, gold nanoparticles, etc. Recent advancements in nanocarrier systems include mesoporous silica nanoparticles (MSNs), metal organic frameworks, and quantum dots. In the present review, various nanocarrier based drug delivery systems, along with their applications in the management of cancer, have been described with special emphasis on MSNs.

Fully Self-Assembled Silica Nanoparticle-Semiconductor Quantum Dot Supra-Nanoparticles and Immunoconjugates for Enhanced Cellular Imaging by Microscopy and Smartphone Camera

There is a growing need for brighter luminescent materials to improve the detection and imaging of biomarkers. Relevant contexts include low-abundance biomarkers and technology-limited applications, where an example of the latter is the emerging use of smartphones and other nonoptimal but low-cost and portable devices for point-of-care diagnostics. One approach to achieving brighter luminescent materials is incorporating multiple copies of a luminescent material into a larger supra-nanoparticle (supra-NP) assembly. Here, we present a facile method for the preparation and immunoconjugation of supra-NP assemblies (SiO₂@QDs) that comprised many quantum dots (QDs) around a central silica nanoparticle (SiO₂ NP). The assembly was entirely driven by spontaneous affinity interactions between the constituent materials, which included imidazoline-functionalized silica nanoparticles, ligand-coated QDs, imidazole-functionalized dextran, and tetrameric antibody complexes (TACs). The physical and optical properties of the SiO₂@QDs were characterized at both the ensemble and single-particle levels. Notably, the optical properties of the QDs were preserved upon assembly into supra-NPs, and single SiO₂@QDs were approximately an order of magnitude brighter than single QDs and nonblinking. In proof-of-concept applications, including selective immunolabeling of breast cancer cells, the SiO₂@QDs provided higher sensitivity and superior signal-to-background ratios whether using research-grade fluorescence microscopy or smartphone-based imaging. Overall, the SiO₂@QDs are promising materials for enhanced bioanalysis and imaging.

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

Differences in toxicity, mitochondrial function and miRNome in human cells exposed in vitro to Cd as CdS quantum dots or ionic Cd

Cadmium is toxic to humans, although Cd-based quantum dots exerts less toxicity. Human hepatocellular carcinoma cells (HepG2) and macrophages (THP-1) were exposed to ionic Cd, Cd(II), and cadmium sulfide quantum dots (CdS QDs), and cell viability, cell integrity, Cd accumulation, mitochondrial function and miRNome profile were evaluated. Cell-type and Cd form-specific responses were found: CdS QDs affected cell viability more in HepG2 than in THP-1; respective IC₂₀ values were ∼3 and ∼50 μg ml^-1. In both cell types, Cd(II) exerted greater effects on viability. Mitochondrial membrane function in HepG2 cells was reduced 70 % with 40 μg ml^-1 CdS QDs but was totally inhibited by Cd(II) at corresponding amounts. In THP-1 cells, CdS QDs has less effect on mitochondrial function; 50 μg ml^-1 CdS QDs or equivalent Cd(II) caused 30 % reduction or total inhibition, respectively. The different in vitro effects of CdS QDs were unrelated to Cd uptake, which was greater in THP-1 cells. For both cell types, changes in the expression of miRNAs (miR-222, miR-181a, miR-142-3p, miR-15) were found with CdS QDs, which may be used as biomarkers of hazard nanomaterial exposure. The cell-specific miRNome profiles were indicative of a more conservative autophagic response in THP-1 and as apoptosis as in HepG2.

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.

Cu-In-S/ZnS@carboxymethylcellulose supramolecular structures: Fluorescent nanoarchitectures for targeted-theranostics of cancer cells

Although the field of oncology nanomedicine has shown indisputable progress in recent years, cancer remains one of the most lethal diseases, where the early diagnosis plays a pivotal role in the patient's prognosis and therapy. Herein, we report for the first time, the synthesis of biocompatible nanostructures composed of Cu-In-S and Cu-In-S/ZnS nanoparticles functionalized with carboxymethylcellulose biopolymer produced by a green aqueous process. These inorganic-organic colloidal nanohybrids developed supramolecular architectures stabilized by chemical functional groups of the polysaccharide shell with the fluorescent semiconductor nanocrystal core, which were extensively characterized by several morphological and spectroscopical techniques. Moreover, these nanoconjugates were covalently bonded with folic acid via amide bonds and electrostatically conjugated with the anticancer drug, producing functionalized supramolecular nanostructures. They demonstrated nanotheranostics properties for bioimaging and drug delivery vectorization effective for killing breast cancer cells in vitro. These hybrids offer a new nanoplatform using fluorescent polysaccharide-drug conjugates for cancer theranostics applications.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles

BACKGROUND

Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs.

OBJECTIVES

By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties.

METHODS

We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta (IL- 1 β ) release in THP-1 cell line. The in vitro hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs.

RESULTS

Seventeen out of 30 MeONPs induced excess IL- 1 β production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (ACC = 86 % ). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive ζ -potential were more likely to cause lysosomal damage and inflammation.

CONCLUSIONS

IL- 1 β released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on IL- 1 β were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508.

Revisiting the cytotoxicity of quantum dots: an in-depth overview

Recently, medical research has been shifting its focus to nanomedicine and nanotherapeutics in the pursuit of drug development research. Quantum dots (QDs) are a critical class of nanomaterials due to their unique properties, which include optical, electronic, and engineered biocompatibility in physiological environments. These properties have made QDs an attractive biomedical resource such that they have found application as both in vitro labeling and in vivo theranostic (therapy-diagnostic) agents. Considerable research has been conducted exploring the suitability of QDs in theranostic applications, but the cytotoxicity of QDs remains an obstacle. Several types of QDs have been investigated over the past decades, which may be suitable for use in biomedical applications if the barrier of cytotoxicity can be resolved. This review attempts to report and analyze the cytotoxicity of the major QDs along with relevant related aspects.

Bioluminescence-Based Energy Transfer Using Semiconductor Quantum Dots as Acceptors

Bioluminescence resonance energy transfer (BRET) is the non-radiative transfer of energy from a bioluminescent protein donor to a fluorophore acceptor. It shares all the formalism of Förster resonance energy transfer (FRET) but differs in one key aspect: that the excited donor here is produced by biochemical means and not by an external illumination. Often the choice of BRET source is the bioluminescent protein Renilla luciferase, which catalyzes the oxidation of a substrate, typically coelenterazine, producing an oxidized product in its electronic excited state that, in turn, couples with a proximal fluorophore resulting in a fluorescence emission from the acceptor. The acceptors pertinent to this discussion are semiconductor quantum dots (QDs), which offer some unrivalled photophysical properties. Amongst other advantages, the QD's large Stokes shift is particularly advantageous as it allows easy and accurate deconstruction of acceptor signal, which is difficult to attain using organic dyes or fluorescent proteins. QD-BRET systems are gaining popularity in non-invasive bioimaging and as probes for biosensing as they don't require external optical illumination, which dramatically improves the signal-to-noise ratio by avoiding background auto-fluorescence. Despite the additional advantages such systems offer, there are challenges lying ahead that need to be addressed before they are utilized for translational types of research.

Biodistribution and acute toxicity of cadmium-free quantum dots with different surface functional groups in mice following intratracheal inhalation

Indium phosphide/zinc sulfate (InP/ZnS) quantum dots (QDs) are presumed to be less hazardous than those that contain cadmium. However, the toxicological profile has not been established. The present study investigated the acute toxicity of InP/ZnS QDs with different surface modifications (COOH, NH₂, and OH) in mice after pulmonary aerosol inhalation. InP/ZnS QDs were able to pass through the blood-gas barrier and enter the circulation, and subsequently accumulated in major organs. No obvious changes were observed in the body weight or major organ coefficients. Red blood cell counts and platelet-related indicators were in the normal range, but the proportion of white blood cells was altered. The InP/ZnS QDs caused varying degrees of changes in some serum markers, but no histopathological abnormalities related to InP/ZnS QDs treatment was observed in major organs except that hyperemia in alveolar septa was found in lung sections. These results suggested that the effects of respiratory exposure to InP/ZnS QDs on the lungs need to be fully considered in future biomedical application although the overall toxicity of quantum dots is relatively low.

Continued Efforts on Nanomaterial-Environmental Health and Safety Is Critical to Maintain Sustainable Growth of Nanoindustry

Nanotechnology is enjoying an impressive growth and the global nanotechnology industry is expected to exceed US$ 125 billion by 2024. Based on these successes, there are notions that enough is known and efforts on engineered nanomaterial environmental health and safety (nano-EHS) research should be put on the back burner. However, there are recent events showing that it is not the case. The US Food and Drug Administration found ferumoxytol (carbohydrate-coated superparamagnetic iron oxide nanoparticle) for anemia treatment could induce lethal anaphylactic reactions. The European Union will categorize TiO2 as a category 2 carcinogen due to its inhalation hazard and France banned use of TiO2 (E171) in food from January 1, 2020 because of its carcinogenic potential. Although nanoindustry is seemingly in a healthy state, growth could be hindered for the lack of certainty and more nano-EHS research is needed for the sustainable growth of nanoindustry. Herein, the current knowledge gaps and the way forward are elaborated.

Targeted delivery of nanomaterials with chemical cargoes in plants enabled by a biorecognition motif

Current approaches for nanomaterial delivery in plants are unable to target specific subcellular compartments with high precision, limiting our ability to engineer plant function. We demonstrate a nanoscale platform that targets and delivers nanomaterials with biochemicals to plant photosynthetic organelles (chloroplasts) using a guiding peptide recognition motif. Quantum dot (QD) fluorescence emission in a low background window allows confocal microscopy imaging and quantitative detection by elemental analysis in plant cells and organelles. QD functionalization with β-cyclodextrin molecular baskets enables loading and delivery of diverse chemicals, and nanoparticle coating with a rationally designed and conserved guiding peptide targets their delivery to chloroplasts. Peptide biorecognition provides high delivery efficiency and specificity of QD with chemical cargoes to chloroplasts in plant cells in vivo (74.6 ± 10.8%) and more specific tunable changes of chloroplast redox function than chemicals alone. Targeted delivery of nanomaterials with chemical cargoes guided by biorecognition motifs has a broad range of nanotechnology applications in plant biology and bioengineering, nanoparticle-plant interactions, and nano-enabled agriculture.

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

The protein corona affects the clinical applications, organ targeting, and safety assessment of nanomaterials, and prediction of the protein corona would be valuable for the design of ideal nanomaterials. However, no methods to predict the protein corona are available. Overcoming the numerous quantitative and qualitative factors influencing corona formation, the present work builds models that precisely predict the functional composition of the protein corona and the cell recognition of nanoparticles (NPs) integrating machine learning and meta-analysis. This workflow provides an effective method to predict the functional composition of the protein corona that determines cell recognition to guide the synthesis and applications of NPs.

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R² (less than 0.40). Here, the performance with R² over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R² (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

Rapid evaluation of gold nanoparticle-lipid membrane interactions using a lipid/polydiacetylene vesicle sensor

Surface modification of gold nanoparticles (AuNPs) has significant and complicated effects on their interactions with cell membranes. In this study, we used a lipid/polyacetylene (PDA) vesicle sensor as the lipid membrane model to evaluate AuNP-lipid membrane interactions. Based on the colorimetric response (CR) of PDA vesicles before and after incubation with AuNPs, it was found that the interaction was highly dependent on the surface charge of AuNPs. As compared to the positively charged NPs, neutral and zwitterionic NPs adsorbed much less on the lipid membrane. Negatively charged NPs did not induce any noticeable color changes even at high concentrations. A class of cationic AuNPs with different degrees of surface hydrophobicity was further selected to investigate the role of hydrophobicity in interacting with lipid/PDA vesicles, and log(EC50) was employed as the evaluation index. According to the log(EC50)-NP concentration curve, the hydrophobicity of NPs enhanced the lipid membrane affinity, but electrostatic interactions weakened this effect. Finally, different concentrations of bovine serum albumin (BSA) were used to study the effect of the protein corona on NP-lipid membrane interactions. The formation of a NP-protein corona was found to mask the electrostatic interactions, leading to the decrease of the CR values of cationic NPs, and highly hydrophobic NPs were less affected by a low concentration of BSA due to the strong hydrophobic interactions. Although the effect of NP surface properties on their interactions with cells is far more complicated, our study provides a rapid and effective method for the evaluation of the interactions between surface modified AuNPs and lipid membranes.

Dextran-Functionalized Quantum Dot Immunoconjugates for Cellular Imaging

Brightly luminescent semiconductor quantum dots (QDs) are ideal materials for cellular imaging and analysis because of their advantageous optical properties and surface area that supports multivalent conjugation of biomolecules. An important design consideration for effective use of these materials is a hydrophilic, biocompatible surface chemistry that provides colloidal stability and minimizes nonspecific interactions with biological molecules and systems. Dextran coatings are able to satisfy these criteria. Despite frequent use of dextran coatings with other nanomaterials (e.g., iron oxide nanoparticles), there has been little development and application of dextran coatings for QDs. In this chapter, we describe methods for the synthesis and characterization of a dextran ligand for QDs, including preparation of an immunoconjugate via tetrameric antibody complexes (TAC). The utility of these immunoconjugates is demonstrated through immunofluorescent labeling and imaging of overexpressed human epidermal growth factor receptor 2 (HER2) on the surface of SK-BR3 breast cancer cells.

Nanoassembled Peptide Biosensors for Rapid Detection of Matrilysin Cancer Biomarker

Early detection of cancer is likely to be one of the most effective means of reducing the cancer mortality rate. Hence, simple and ultra-quick methods for noninvasive detection of early-stage tumors are highly sought-after. In this study, a nanobiosensing platform with a rapid response time of nearly 30 s is introduced for the detection of matrilysin-the salivary gland cancer biomarker-with a limit of detection as low as 30 nm. This sensing platform is based on matrilysin-digestible peptides that bridge gold nanoparticle (AuNPs) cores (≈30-50 nm) and carbon quantum dot (CDs) satellites (≈9 nm). A stepwise synthesis procedure is used for self-assembly of AuNP-peptide-CDs, ensuring their long-term stability. The AuNP-peptide-CDs produce ideal optical signals, with noticeable fluorescence quenching effects. Upon peptide cleavage by matrilysin, CDs leave the surface of AuNPs, resulting in ultra-fast detectable violet and visible fluorescent signals.