Strategies for measuring concentrations and forms of amyloid-ß peptides.

Alzheimer's disease (AD) is affecting more and more people worldwide without the effective treatment, while the existed pathological mechanism has been confirmed barely useful in the treatment. Amyloid-ß peptide (Aß), a main component of senile plaque, is regarded as the most promising target in AD treatment. Aß clearance from AD brain seems to be a reliably therapeutic strategy, as the two exited drugs, GV-971 and aducanumab, are both developed based on it. However, doubt still exists. To exhaustive expound on the pathological mechanism of Aß, rigorous analyses on the concentrations and aggregation forms are essential. Thus, it is attracting broad attention these years. However, most of the sensors have not been used in pathological studies, as the lack of the bridge between analytical chemist and pathologists. In this review, we made a brief introduce on Aß-related pathological mechanism included in ß-amyloid hypothesis to elucidate the detection conditions of sensor methods. Furthermore, a summary of the sensor methods was made, which were based on Aß concentrations and form detections that have been developed in the past 10 years. As the greatest number of the sensors were built on fluorescent spectroscopy, electrochemistry, and Roman spectroscopy, detailed elucidation on them was made. Notably, the aggregation process is another important factor in revealing the progress of AD and developing the treatment methods, so the sensors on monitoring Aß aggregation processes were also summarized.

Fluorinated bamboo-structure carbon nanotubes: as attractive substrates for the cathodes of lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries have gained considerable attention for high theoretical specific capacity and energy density. However, their development is hampered by the poor electrical conductivity of sulfur and the shuttle of polysulfides. Herein, the acidified bamboo-structure carbon nanotubes (BCNTs) were mixed with polyvinylidene difluoride and pyrolyzed at high-temperature to obtain the fluorinated bamboo-structure carbon nanotubes (FBCNTs), which were compounded with sulfur as the cathode. The prepared S@FBCNTs with sulfur loading reaching 74.2 wt.% shows a high initial specific capacity of 1407.5 mAh·g-1at the discharge rate of 0.1 C. When the discharge rate was increased to 5 C, the capacity could be maintained at 622.3 mAh·g-1. The electrical conductivity of carbon nanotubes is effectively improved by semi-ionic C-F bonds formed by the doped F atoms and carbon atoms. Simultaneously, the surface of the F-containing carbon tubes exhibits strong polarity and strong chemisorption effect on polysulfides, which inhibits the shuttle effect of Li-S batteries.

Guluronic acid can inhibit copper(II) and amyloid - ß peptide coordination and reduce copper-related reactive oxygen species formation associated with Alzheimer's disease.

Copper-related reactive oxygen species (ROS) formation can lead to neuropathologic degradation associated with Alzheimer's disease (AD) according to amyloid cascade hypothesis. A complexing agent that can selectively chelate with copper ions and capture copper ions from the complex formed by copper ions and amyloid-ß (Cu - Aß complex) may be available in reducing ROS formation. Herein, we described applications of guluronic acid (GA), a natural oligosaccharide complexing agent obtained from enzymatic hydrolysis of brown algae, in reducing copper-related ROS formation. UV-vis absorption spectra demonstrated the coordination between GA and Cu(II). Ascorbic acid consumption and coumarin-3-carboxylic acid fluorescence assays confirmed the viability of GA in reducing ROS formation in solutions containing other metal ions and Aß. Fluorescence kinetics, DPPH radical clearance and high resolution X - ray photoelectron spectroscopy results revealed the reductivity of GA. Human liver hepatocellular carcinoma (HepG2) cell viability demonstrated the biocompatibility of GA at concentrations lower than 320 µM. Cytotoxic results of human neuroblastoma (SH-SY5Y) cells verified that GA can inhibit copper-related ROS damage in neuronal cells. Our findings, combined with the advantages of marine drugs, make GA a promising candidate in reducing copper-related ROS formation associated with AD therapy.

Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein-Protein Interactions From Time-Series Phosphoproteomic Data.

Analysis of high-throughput omics data is one of the most important approaches for obtaining information regarding interactions between proteins/genes. Time-series omics data are a series of omics data points indexed in time order and normally contain more abundant information about the interactions between biological macromolecules than static omics data. In addition, phosphorylation is a key posttranslational modification (PTM) that is indicative of possible protein function changes in cellular processes. Analysis of time-series phosphoproteomic data should provide more meaningful information about protein interactions. However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. Moreover, most reported tools ignore the lag between functional alterations and the corresponding changes in protein synthesis/PTM and are highly dependent on previous knowledge, resulting in high false-positive rates and difficulties in finding newly discovered protein-protein interactions (PPIs). Therefore, in the present study, we developed a new method to discover protein-protein interactions with the delayed comparison and Apriori algorithm (DCAA) to address the aforementioned problems. DCAA is based on the idea that there is a lag between functional alterations and the corresponding changes in protein synthesis/PTM. The Apriori algorithm was used to mine association rules from the relationships between items in a dataset and find PPIs based on time-series phosphoproteomic data. The advantage of DCAA is that it does not rely on previous knowledge and the PPI database. The analysis of actual time-series phosphoproteomic data showed that more than 68% of the protein interactions/regulatory relationships predicted by DCAA were accurate. As an analytical tool for PPIs that does not rely on a priori knowledge, DCAA should be useful to predict PPIs from time-series omics data, and this approach is not limited to phosphoproteomic data.