Versatile Self-Assembly of Water-Soluble Thiol-Capped CdTe Quantum Dots: External Destabilization and Internal Stability of Colloidal QDs

In this paper, we report on the versatile self-assembly of water-soluble thiol-capped CdTe quantum dots (QDs), nanoparticles (NPs), or nanocrystals induced by l-cysteine (l-Cys). Major efforts are focused on the control of the self-organization of QDs into nanosheets (NSs), for example, by altering the solution pH and the QD size. The as-prepared nanosheets exhibit bright photoluminescence (PL) and retain the size-quantized properties of initial CdTe QDs, since they are actually formed by a 2D network of assembled QDs. By optical techniques, TEM, EDX, powder XRD, etc., it is found that the unique l-Cys-induced external destabilization is responsible for the template-free self-organization process, with the further assistance of the specific NP–NP interactions. And the internal chemical stability of initial CdTe QDs also is proven for the first time to play an important role. These results help to enhance the current understanding about the mechanism for the destabilization of colloidal NPs and their self-assembly behavior

Quaternary Zn–Ag–In–Se Quantum Dots for Biomedical Optical Imaging of RGD-Modified Micelles

Exploring the synthesis of new biocompatible quantum dots (QDs) helps in overcoming the intrinsic toxicity of the existing QDs composed of highly toxic heavy metals (e.g., Cd, Hg, Pb, etc.) and is particularly interesting for the future practical application of QDs in biomedical imaging. Hence, in this report, a new one-pot approach to oil-soluble (highly toxic heavy metal-free) highly luminescent quaternary Zn–Ag–In–Se (ZAISe) QDs was designed. Their photoluminescence (PL) emission could be systematically tuned from 660 to 800 nm by controlling the Ag/Zn feed ratio, and their highest PL quantum yield is close to 50% after detailed optimization. Next, by using biodegradable RGD peptide (arginine–glycine–aspartic acid)-modified N-succinyl-N′-octyl-chitosan (RGD-SOC) micelles as a water transfer agent, the versatility of these quaternary ZAISe QDs for multiscale bioimaging of micelles (namely, in vitro and in vivo evaluating the tumor targeting of drug carriers) was further explored, as a promising alternative for Cd- and Pb-based QDs

Additional file 3 of Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

Additional file 3: Table S1. Identification of differentially expressed genes

Additional file 6 of Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

Additional file 6: Table S4. Survival information of enrolled CCRCC patients

Additional file 5 of Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

Additional file 5: Table S3. Distribution of lncrnas per module

Additional file 4 of Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

Additional file 4: Table S2. Necroptosis-related genes

Additional file 2 of Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

Additional file 2: Figure S2. The details of copy number amplification and deletion in the genome in the three subgroups

Additional file 1 of Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

Additional file 1: Figure S1. Graphs of scale independence, mean connectivity and scale-free topology

MOESM2 of Learning important features from multi-view data to predict drug side effects

Additional file 2. Additional tables

MOESM1 of Learning important features from multi-view data to predict drug side effects

Additional file 1. Analysis of the proposed method and additional figures