CONSPECTUS: Photon upconversion nanoparticles (UCNPs) have emerged as a promising new class of nanomaterials due to their ability to convert near-IR light into visible luminescence. Unfortunately, most efficient methods for preparing UCNPs yield hydrophobic materials, but water-dispersibility is needed in the major fields of applications of UCNPs, that is, in bioimaging, labeling, and bioassays. Numerous methods therefore have been reported in the past years to convert the hydrophobic surface of UCNPs to a more hydrophilic one so to render them dispersible in aqueous systems. We present a classification respective for these strategies and assess the main methods. These include (A) chemical modification of the hydrophobic (typically oleate) ligand on the surface, (B) addition of an extra layer, (C) addition of a thin shell on top of the UCNP, and (D) complete replacement of the original ligand by another one. Chemical modification (A) involves oxidation of the oleate or oleylamine and leads to particles with terminal oxygen functions. This method is less often used because solutions of the resulting UCNPs in water have limited colloidal stability, protocols are time-consuming and often give low yields, and only a limited number of functional groups can be introduced. Methods B and C involve coating of UCNPs with amphiphiles or with shells made from silica oxide, titanium oxide, or metallic gold or silver. These methods are quite versatile in terms of further modifications, for example, by further cross-linking or by applying thiol-gold chemistry. Growing an extra shell is, however, often accompanied by a higher polydispersity. Method D can be divided into subgroups based on either (i) the direct (single-step) replacement of the native ligand by a new ligand or (ii) two-step protocols using nitrosyltetrafluoroborate (NOBF4) or strong acids as reagents to produce ligand-free UCNPs prior to the attachment of a new ligand. These methods are simple and versatile, and the distance between the new ligand and the luminescent particle can be well controlled. However, the particles often have limited stability in buffer systems. The methods described also are of wider interest because they are likely to be applicable to other kinds of nanomaterials. We additionally address the need for (a) a better control of particle size and homogeneity during synthesis, (b) more reproducible methods for surface loading and modification, (c) synthetic methods giving higher yields of UCNPs, (d) materials displaying higher quantum yields in water solution without the need for tedious surface modifications, (e) improved methods for workup (including the suppression of aggregation), (f) new methods for surface characterization, and (g) more affordable reagents for use in surface modification. It is noted that most synthetic research in the area is of the trial-and-error kind, presumably due to the lack of understanding of the mechanisms causing current limitations. Finally, all particles are discussed in terms of their biocompatibility (as far as data are available), which is quintessential in terms of imaging, the largest field of application.
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http://dx.doi.org/10.1021/ar500253g | DOI Listing |
Genet Med
January 2025
Genomics Ethics, and Translational Research Program, RTI International, Research Triangle Park, NC; Department of Translational and Applied Genomics, Kaiser Permanente Center for Health Research, Portland, OR. Electronic address:
Purpose: Limited evidence evaluates parents' perceptions of their child's clinical genomic sequencing (GS) results, particularly among individuals from medically underserved groups. Five Clinical Sequencing Evidence-Generating Research (CSER) consortium studies performed GS in children with suspected genetic conditions with high proportions of individuals from underserved groups to address this evidence gap.
Methods: Parents completed surveys of perceived understanding, personal utility, and test-related distress after GS result disclosure.
Genet Med
January 2025
Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada; Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa. Electronic address:
Purpose: Universal newborn hearing screening (UNHS) programs using audiometric techniques alone are limited in ability to detect non-congenital childhood permanent hearing loss (PHL). In 2019, Ontario launched universal newborn screening (NBS) for PHL risk factors: congenital cytomegalovirus (cCMV) and 22 common variants in GJB2 and SLC26A4. Here we describe our experience with genetic risk factor screening.
View Article and Find Full Text PDFObjective: Scleroderma-associated autoantibodies (SSc-Abs) are specific in participants (pts) with systemic sclerosis and are associated with organ involvement. Our objective was to assess the influence of baseline SSc-Abs on the trajectories of the clinical outcome assessments (COAs) in a phase III randomized controlled trial.
Methods: We used data on both the groups who received placebo (Pbo) and tocilizumab from the focuSSced trial.
Dalton Trans
January 2025
School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
In this work, we successfully prepared four POM-based organic-inorganic hybrids, namely, [(CHN)(CHN)][PMoO] (1), [(CHN)(CHN)][PMoO] (2), [(CHN)][PMoO]·4HO (3), and [(CHN)][PMoO] (4) (where CHN = pyridine, CHN = pyrazine, CHN = 2,7-diamino-1,3,4,6,8,9-hexaazaspiro[4.4] nonane, and CHN = 3-amino-1,2,4-triazole), using a hydrothermal method. Compounds 1 and 2 exhibited a lamellar three-dimensional structure.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
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