Structures, gelatinization properties and enzyme hydrolyses of starches from transparent and floury grains of rices subjected to field natural extreme high temperature.

Three rice varieties underwent the field natural extreme high temperature (EHT) with daily average temperature over 30 °C from 21 to 89 days after sowing, and had transparent, chalky and floury grains. The structures, gelatinization properties and enzyme hydrolyses of starches from transparent and floury grains were investigated. Compared with control transparent grains, floury grains subjected to EHT markedly decreased the contents of amylose molecules, amylopectin A chains and amylopectin B1 chains and increased the contents of amylopectin B2 and B3+ chains and the average branch-chain length of amylopectin. Both transparent and floury grains had A-type starches, but floury grain starches exhibited higher relative crystallinity, gelatinization temperature, retrogradation and pasting viscosities than transparent grain starches. Floury grain starches had lower hydrolysis rates than transparent grain starches. Native starches were more resistant to digestion but gelatinized and retrograded starches were more prone to digestion in floury grains than in transparent grains.

An enzyme-powered microRNA discriminator for the subtype-specific diagnosis of breast cancer.

Breast cancer, a disease with highly heterogeneous features, is the most common malignancy diagnosed in people worldwide. Early diagnosis of breast cancer is crucial for improving its cure rate, and accurate classification of the subtype-specific features is essential to precisely treat the disease. An enzyme-powered microRNA (miRNA, RNA = ribonucleic acid) discriminator was developed to selectively distinguish breast cancer cells from normal cells and further identify subtype-specific features. Specifically, miR-21 was used as a universal biomarker to discriminate between breast cancer cells and normal cells, and miR-210 was used to identify triple-negative subtype features. The experimental results demonstrated that the enzyme-powered miRNA discriminator displayed low limits of detection at fM levels for both miR-21 and miR-210. Moreover, the miRNA discriminator enabled the discrimination and quantitative determination of breast cancer cells derived from different subtypes based on their miR-21 levels, and the further identification of the triple-negative subtype in combination with the miR-210 levels. Therefore, it is hoped that this study will provide insight into subtype-specific miRNA profiling, which may have potential use in the clinical management of breast tumours based on their subtype characteristics.

Application of functional peptides in the electrochemical and optical biosensing of cancer biomarkers.

Early screening and diagnosis are the most effective ways to prevent the occurrence and progression of cancers, thus many biosensing strategies have been developed to achieve economic, rapid, and effective detection of various cancer biomarkers. Recently, functional peptides have been gaining increasing attention in cancer-related biosensing due to their advantageous features of a simple structure, ease of synthesis and modification, high stability, and good biorecognition, self-assembly and antifouling capabilities. Functional peptides can not only act as recognition ligands or enzyme substrates for the selective identification of different cancer biomarkers but also function as interfacial materials or self-assembly units to improve the biosensing performances. In this review, we summarize the recent advances in functional peptide-based biosensing of cancer biomarkers according to the used techniques and the roles of peptides. Particular attention is focused on the use of electrochemical and optical techniques, both of which are the most commonly used techniques in the field of biosensing. The challenges and promising prospects of functional peptide-based biosensors in clinical diagnosis are also discussed.

A deep learning model for predicting selected organic molecular spectra.

Accurate and efficient molecular spectra simulations are crucial for substance discovery and structure identification. However, the conventional approach of relying on the quantum chemistry is cost intensive, which hampers efficiency. Here we develop DetaNet, a deep-learning model combining E(3)-equivariance group and self-attention mechanism to predict molecular spectra with improved efficiency and accuracy. By passing high-order geometric tensorial messages, DetaNet is able to generate a wide variety of molecular properties, including scalars, vectors, and second- and third-order tensors-all at the accuracy of quantum chemistry calculations. Based on this we developed generalized modules to predict four important types of molecular spectra, namely infrared, Raman, ultraviolet-visible, and 1H and 13C nuclear magnetic resonance, taking the QM9S dataset containing 130,000 molecular species as an example. By speeding up the prediction of molecular spectra at quantum chemical accuracy, DetaNet could help progress toward real-time structural identification using spectroscopic measurements.