Analyzing the surface of functional nanomaterials-how to quantify the total and derivatizable number of functional groups and ligands

Emerging advances in nano-biomaterial assisted amyloid beta chimeric antigen receptor macrophages (CAR-M) therapy: reducing plaque burden in Alzheimer's disease

Alzheimer's disease is the most common form, accounting for 60-70% of 55 million dementia cases. Even though the precise pathophysiology of AD is not completely understood, clinical trials focused on antibodies targeting aggregated forms of β amyloid (Aβ) have demonstrated that reducing amyloid plaques can arrest cognitive decline in patients in the early stages of AD. In this study, we provide an overview of current research and innovations for controlled release from nano-biomaterial-assisted chimeric antigen receptor macrophage (CAR-M) therapeutic strategies targeted at AD. Nano-bio materials, such as iron-oxide nanoparticles (IONPs), can be made selectively (Hp-Hb/mannose) to bind and take up Aβ plaques like CAR-M cells. By using nano-bio materials, both the delivery and stability of CAR-M cells in brain tissue can be improved to overcome the barriers of the BBB and enhance therapeutic effects. By enhancing the targeting capabilities and stability of CAR-M cells, mRNA-loaded nano-biomaterials can significantly improve the efficacy of immunotherapy for plaque reduction in AD. This novel strategy holds promise for translating preclinical successes into clinical applications, potentially revolutionising the management of AD.

Influence of Surface Chemistry on Metal Deposition Outcomes in Copper Selenide-Based Nanoheterostructure Synthesis

The use of nanoparticle surface chemistry to direct metal deposition has been well-studied in the modification of metal nanoparticle substrates but is not yet well-established for metal chalcogenide particle substrates, although integration of these particles into nanoheterostructures is of high interest. In this report, we investigate the effect of Cu2-xSe surface chemistry on the morphology of metal deposition on these plasmonic semiconductor nanoparticles. Specifically, we functionalize Cu2-xSe nanoparticles with a suite of 12 different ligands and investigate how different aspects of the ligand structure do or do not impact the morphology and extent of subsequent metal deposition on the Cu2-xSe surface. Surprisingly, our results indicate that the morphology of the resulting metal deposits and the extent of metal deposition onto the existing Cu2-xSe particle substrate are indistinguishable for the majority of ligands tested. An exception to these findings is observed for particles functionalized by quaternary alkylammonium bromides, which exhibit statistically distinct metal deposition patterns compared to all other ligands tested. We hypothesize that this unique behavior is due to a cooperative binding mechanism of the quaternary alkylammonium bromides to the surface of copper selenide. Taken together, these results yield both new strategies for controlling postsynthetic modification of copper selenide nanoparticles and also reveal limitations of surface chemistry-based approaches for this system.

Polymeric nanocarrier-based adjuvants to enhance a locally produced mucosal coryza vaccine in chicken

Infectious coryza (IC) is an acute upper respiratory disease of chicken caused by Avibacterium (A.) paragallinarum. This disease results in an increased culling rate in meat chicken and a marked decrease in egg production (10% to more than 40%) in laying and breeding hens. Vaccines were first used against IC and effectively controlled the disease. Nanotechnology provides an excellent way to develop a new generation of vaccines. NPs have been widely used in vaccine design as adjuvants and antigen delivery vehicles and as antibacterial agents; thus, they can be used as inactivators for bacterial culture. In this research, the antibacterial effects of several nanoparticles (NPs), such as silicon dioxide with chitosan (SiO2-CS), oleoyl-chitosan (O.CS), silicon dioxide (SiO2), and iron oxide (Fe3O4), on A. paragallinarum were studied. Additionally, different A. paragallinarum vaccines were made using the same nanomaterials at a concentration of 400 µg/ml to help control infectious coryza disease in chicken. A concentration of 400 µg/ml of all the NPs tested was the best concentration for the inactivation of A. paragallinarum. Additionally, this study showed that the infectious coryza vaccine adjuvanted with SiO2 NPs had the highest immune response, followed by the infectious coryza vaccine adjuvanted with Fe3O4 NPs, the infectious coryza vaccine adjuvanted with SiO2-CS NPs, and the infectious coryza vaccine adjuvanted with O.CS NPs in comparison with the infectious coryza vaccine adjuvanted with liquid paraffin (a commercial vaccine).

Efficient synthesis of benzoacridines and indenoquinolines catalyzed by acidic magnetic dendrimer

A novel solid acid catalyst with recoverability, named as Fe3O4@SiO2@TAD-G2-SO3H, was successfully synthesized by immobilizing sulfonic acid groups on triazine dendrimer-modified magnetic nanoparticles. This nanomaterial structure and composition were thoroughly characterized using various analytical techniques, including thermogravimetric analysis (TGA), elemental analysis, Fourier transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDX), elemental mapping, acid-base titration, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and vibrating sample magnetometry (VSM). The acid-decorated magnetic dendrimer was served as a highly effective catalyst for the synthesis of tetrahydrobenzo[c]acridin-8(9H)-one and benzo[h]indeno[1,2-b]quinoline-8-one derivatives. The reaction proceeded smoothly under mild conditions through the one-pot condensation of aromatic aldehydes, 1-naphthylamine, and either dimedone or 1,3-indanedione, affording the desired products in high yields ranging from 90 to 96%. The catalyst was easily separated from the reaction mixture by employing a magnetic field, allowing for its recycling up to five times with slight loss in its activity (only 10%). Nearly, quantitative recovery of catalyst (up to 95%) could be obtained from each run. So, this catalyst facilitates the reaction progress and simplifies the purification process. Other remarkable features of this method are operational simplicity, excellent yields, mild condition, and a wide range of substrate applicability.

Control of Luminescence and Interfacial Properties as Perspective for Upconversion Nanoparticles

Near-infrared (NIR) light is highly suitable for studying biological systems due to its minimal scattering and lack of background fluorescence excitation, resulting in high signal-to-noise ratios. By combining NIR light with lanthanide-based upconversion nanoparticles (UCNPs), upconversion is used to generate UV or visible light within tissue. This remarkable property has gained significant research interest over the past two decades. Synthesis methods are developed to produce particles of various sizes, shapes, and complex core-shell architectures and new strategies are explored to optimize particle properties for specific bioapplications. The diverse photophysics of lanthanide ions offers extensive possibilities to tailor spectral characteristics by incorporating different ions and manipulating their arrangement within the nanocrystal. However, several challenges remain before UCNPs can be widely applied. Understanding the behavior of particle surfaces when exposed to complex biological environments is crucial. In applications where deep tissue penetration is required, such as photodynamic therapy and optogenetics, UCNPs show great potential as nanolamps. These nanoparticles can combine diagnostics and therapeutics in a minimally invasive, efficient manner, making them ideal upconversion probes. This article provides an overview of recent UCNP design trends, highlights past research achievements, and outlines potential future directions to bring upconversion research to the next level.

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer

Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.

Molecular Surface Quantification of Multifunctionalized Gold Nanoparticles Using UV-Visible Absorption Spectroscopy Deconvolution

Multifunctional gold nanoparticles (AuNPs) are of great interest, owing to their vast potential for use in many areas including sensing, imaging, delivery, and medicine. A key factor in determining the biological activity of multifunctional AuNPs is the quantification of surface conjugated molecules. There has been a lack of accurate methods to determine this for multifunctionalized AuNPs. We address this limitation by using a new method based on the deconvolution and Levenberg-Marquardt algorithm fitting of UV-visible absorption spectrum to calculate the precise concentration and number of cytochrome C (Cyt C) and zinc porphyrin (Zn Porph) bound to each multifunctional AuNP. Dynamic light scattering (DLS) and zeta potential measurements were used to confirm the functionalization of AuNPs with Cyt C and Zn Porph. Transmission electron microscopy (TEM) was used in conjunction with UV-visible absorption spectroscopy and DLS to identify the AuNP size and confirm that no aggregation had taken place after functionalization. Despite the overlapping absorption bands of Cyt C and Zn Porph, this method was able to reveal a precise concentration and number of Cyt C and Zn Porph molecules attached per AuNP. Furthermore, using this method, we were able to identify unconjugated molecules, suggesting the need for further purification of the sample. This guide provides a simple and effective method to quickly quantify molecules bound to AuNPs, giving users valuable information, especially for applications in drug delivery and biosensors.

Influence of nanoparticle encapsulation and encoding on the surface chemistry of polymer carrier beads

Surface-functionalized polymer beads encoded with molecular luminophores and nanocrystalline emitters such as semiconductor nanocrystals, often referred to as quantum dots (QDs), or magnetic nanoparticles are broadly used in the life sciences as reporters and carrier beads. Many of these applications require a profound knowledge of the chemical nature and total number of their surface functional groups (FGs), that control bead charge, colloidal stability, hydrophobicity, and the interaction with the environment and biological systems. For bioanalytical applications, also the number of groups accessible for the subsequent functionalization with, e.g., biomolecules or targeting ligands is relevant. In this study, we explore the influence of QD encoding on the amount of carboxylic acid (COOH) surface FGs of 2 µm polystyrene microparticles (PSMPs). This is done for frequently employed oleic acid and oleylamine stabilized, luminescent core/shell CdSe QDs and two commonly used encoding procedures. This included QD addition during bead formation by a thermally induced polymerization reaction and a post synthetic swelling procedure. The accessible number of COOH groups on the surface of QD-encoded and pristine beads was quantified by two colorimetric assays, utilizing differently sized reporters and electrostatic and covalent interactions. The results were compared to the total number of FGs obtained by a conductometric titration and Fourier transform infrared spectroscopy (FTIR). In addition, a comparison of the impact of QD and dye encoding on the bead surface chemistry was performed. Our results demonstrate the influence of QD encoding and the QD-encoding strategy on the number of surface FG that is ascribed to an interaction of the QDs with the carboxylic acid groups on the bead surface. These findings are of considerable relevance for applications of nanoparticle-encoded beads and safe-by-design concepts for nanomaterials.

Quantum Annealing Boosts Prediction of Multimolecular Adsorption on Solid Surfaces Avoiding Combinatorial Explosion

Quantum annealing has been used to predict molecular adsorption on solid surfaces. Evaluation of adsorption, which takes place in all solid surface reactions, is a crucially important subject for study in various fields. However, predicting the most stable coordination by theoretical calculations is challenging for multimolecular adsorption because there are numerous candidates. This report presents a novel method for quick adsorption coordination searches using the quantum annealing principle without combinatorial explosion. This method exhibited much faster search and more stable molecular arrangement findings than conventional methods did, particularly in a high coverage region. We were able to complete a configurational prediction of the adsorption of 16 molecules in 2286 s (including 2154 s for preparation, only required once), whereas previously it has taken 38 601 s. This approach accelerates the tuning of adsorption behavior, especially in composite materials and large-scale modeling, which possess more combinations of molecular configurations.

Revisiting carboxylic group functionalization of silica sol-gel materials

The carboxylic chemical group is a ubiquitous moiety present in amino acids, a ligand for transition metals, a colloidal stabilizer, and a weak acidic ion-exchanger in polymeric resins and given this property, it is attractive for responsive materials or nanopore-based gating applications. As the number of uses increases, subtle requirements are imposed on this molecular group when anchored to various platforms for the functioning of an integrated chemical system. In this context, silica stands as an inert and multipurpose platform that enables the anchoring of multiple chemical entities combined through several orthogonal synthesis methods on the interface. Surface chemical modification relies on the use of organoalkoxysilanes that must meet the demand of tuned chemical properties; this, in turn, urges for innovative approaches for having an improved, but simple, organic toolbox. Starting from commonly available molecular precursors, several approaches have emerged: hydrosilylation, click thiol-ene additions, the use of carbodiimides or the reaction between cyclic anhydrides and anchored amines. In this review, we analyze the importance of the COOH groups in the area of materials science and the commercial availability of COOH-based silanes and present new approaches for obtaining COOH-based organoalkoxide precursors. Undoubtedly, this will attract widespread interest for the ultimate design of highly integrated chemical platforms.

Physical Characterization and Cellular Toxicity Studies of Commercial NiO Nanoparticles

Nickel oxide (NiO) nanoparticles from several manufacturers with different reported sizes and surface coatings were characterized prior to assessing their cellular toxicity. The physical characterization of these particles revealed that sizes often varied from those reported by the supplier, and that particles were heavily agglomerated when dispersed in water, resulting in a smaller surface area and larger hydrodynamic diameter upon dispersion. Cytotoxicity testing of these materials showed differences between samples; however, correlation of these differences with the physical properties of the materials was not conclusive. Generally, particles with higher surface area and smaller hydrodynamic diameter were more cytotoxic. While all samples produced an increase in reactive oxygen species (ROS), there was no correlation between the magnitude of the increase in ROS and the difference in cytotoxicity between different materials.