Traceable Nanocluster-Prodrug Conjugate for Chemo-photodynamic Combinatorial Therapy of Non-small Cell Lung Cancer.

In cancer treatment, image-guided combinatorial therapy is usually a more promising approach than conventional therapy because it may overcome the drawbacks of conventional cancer treatment, such as tumor recurrence and multidrug resistance. To achieve a high therapeutic effect in image-guided combinatorial therapy, the therapeutic material should be traceable, biocompatible, and yet highly effective in eradicating tumors. For this purpose, we developed a traceable nanocarrier consisting of atomically precise gold nanoclusters (Au NCs, Au(SG), abbreviated as Au NCs, where SG stands for glutathione) and a biopolymer (.

Perception of service quality, satisfaction, and behavioral intentions in Ayurveda healthcare.

Background: Based on the previous literature it is confirmed that Performance-based service quality and patient satisfaction are major antecedents of behavioral intentions in the healthcare sector. Here, the study deals with the same variables under the framework of Ayurveda healthcare.

Objectives: The study is an attempt to understand the perceptional differences of healthcare consumers in Ayurveda, by analyzing the relationship between the service quality, satisfaction, and behavioral intentions in Ayurveda.

Flow-through colorimetric assay for detection of nucleic acids in plasma.

A flow-through colorimetric assay for detection of nucleic acids in plasma is reported. The proposed assay features an array of four polyvinylidene fluoride (PVDF) membranes impregnated with cationic poly (3-alkoxy-4-methylthiophene) (PT) as an optical reporter. The sensing strategy is based on monitoring the changes in optical properties of PT, upon complexation with target nucleic acids in the presence and in the absence of their corresponding complementary peptide nucleic acids (PNAs).

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

Background: Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.

SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.

Background: Heme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed.