Facile and versatile ligand analysis method of colloidal quantum dot

Colloidal quantum-dots (QDs) are highly attractive materials for various optoelectronic applications owing to their easy maneuverability, high functionality, wide applicability, and low cost of mass-production. QDs usually consist of two components: the inorganic nano-crystalline particle and organic ligands that passivate the surface of the inorganic particle. The organic component is also critical for tuning electronic properties of QDs as well as solubilizing QDs in various solvents. However, despite extensive effort to understand the chemistry of ligands, it has been challenging to develop an efficient and reliable method for identifying and quantifying ligands on the QD surface. Herein, we developed a novel method of analyzing ligands in a mild yet accurate fashion. We found that oxidizing agents, as a heterogeneous catalyst in a different phase from QDs, can efficiently disrupt the interaction between the inorganic particle and organic ligands, and the subsequent simple phase fractionation step can isolate the ligand-containing phase from the oxidizer-containing phase and the insoluble precipitates. Our novel analysis procedure ensures to minimize the exposure of ligand molecules to oxidizing agents as well as to prepare homogeneous samples that can be readily analyzed by diverse analytical techniques, such as nuclear magnetic resonance spectroscopy and gas-chromatography mass-spectrometry. © 2021, The Author(s).1

AI-based automated Meibomian gland segmentation, classification and reflection correction in infrared Meibography

Purpose: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images. Methods: A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multiple revisions by investigators and graded 6 times by meibomian gland dysfunction (MGD) experts. Two deep learning (DL) models were trained separately to segment areas of the MG and eyelid. Those segmentation were used to estimate MG ratio and meiboscores using a classification-based DL model. A generative adversarial network was implemented to remove specular reflections from original images. Results: The mean ratio of MG calculated by investigator annotation and DL segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34% vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01% accuracy for meiboscore classification on validation set and 59.17% accuracy when tested on images from independent center, compared to 53.44% validation accuracy by MGD experts. The DL-based approach successfully removes reflection from the original MG images without affecting meiboscore grading. Conclusions: DL with infrared meibography provides a fully automated, fast quantitative evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and meiboscore) which are sufficiently accurate for diagnosing dry eye disease. Also, the DL removes specular reflection from images to be used by ophthalmologists for distraction-free assessment.Comment: 11 pages, 13 Figures, 5 Supplementary Figure

Arvelexin Inhibits Colonic Inflammation by Suppression of NF-κB Activation in Dextran Sulfate Sodium-Induced Mice and TNF-α-Induced Colonic Epithelial Cells

Recently, we reported the anti-inflammatory effects of arvelexin isolated from <i>Brassica rapa</i> in macrophages. In the present study, the effects of arvelexin were investigated in a dextran sulfate sodium (DSS)-induced colitis mouse model and in a cellular model. In the DSS-induced colitis model, arvelexin significantly reduced the severity of colitis, as assessed by disease activity, colonic damage, neutrophil infiltration, and levels of colonic iNOS. Moreover, arvelexin inhibited the expressions of IL-8, IP-10, ICAM-1, and VCAM-1 in HT-29 colonic epithelial cells. Arvelexin also inhibited the TNF-α-induced adhesion of U937 monocytic cells to HT-29 cells. Furthermore, arvelexin reduced p65 NF-κB subunit translocation to the nucleus and IκBα degradation in the colonic tissues and in TNF-α-induced HT-29 cells. These results demonstrate that the ameliorative effects of arvelexin on colonic injury are mainly related to its ability to inhibit the inflammatory responses via NF-κB inactivation, and support its possible therapeutic role in colitis

Landscape of actionable genetic alterations profiled from 1,071 tumor samples in Korean cancer patients

Purpose With the emergence of next-generation sequencing (NGS) technology, profiling a wide range of genomic alterations has become a possibility resulting in improved implementation of targeted cancer therapy. In Asian populations, the prevalence and spectrum of clinically actionable genetic alterations has not yet been determined because of a lack of studies examining high-throughput cancer genomic data. Materials and Methods To address this issue, 1,071 tumor samples were collected from five major cancer institutes in Korea and analyzed using targeted NGS at a centralized laboratory. Samples were either fresh frozen or formalin-fixed, paraffin embedded (FFPE) and the quality and yield of extracted genomic DNA was assessed. In order to estimate the effect of sample condition on the quality of sequencing results, tissue preparation method, specimen type (resected or biopsied) and tissue storage time were compared. Results We detected 7,360 non-synonymous point mutations, 1,164 small insertions and deletions, 3,173 copy number alterations, and 462 structural variants. Fifty-four percent of tumors had one or more clinically relevant genetic mutation. The distribution of actionable variants was variable among different genes. Fresh frozen tissues, surgically resected specimens, and recently obtained specimens generated superior sequencing results over FFPE tissues, biopsied specimens, and tissues with long storage duration. Conclusion In order to overcome, challenges involved in bringing NGS testing into routine clinical use, a centralized laboratory model was designed that could improve the NGS workflows, provide appropriate turnaround times and control costs with goal of enabling precision medicine