Inhibition of Protein Tyrosine Phosphatase 1B by Polyphenol Natural Products: Relevant to Diabetes Management

Many biologically active polyphenols have been recognized for their beneficial effects in managing diabetes and their complications. However, the mechanisms behind their functions are poorly understood. As protein-tyrosine phosphatase 1B (PTP1B) has been identified as a target for anti-diabetic agents, the potential inhibitory effects of a dozen structurally diverse polyphenol natural products have been investigated. Among these polyphenols, potent inhibitory activities have been identified for 6 of them with IC50 in micromolar range, while the other polyphenols showed very weak inhibition. A structure-activity relationship (SAR) study and molecular ducking results suggest that both a rigid planar 3-ring backbone and appropriate substitutions of hydroxyl groups benefit the inhibitory activity. The mechanism of inhibition of PTP1B was further investigated by Michaelis-Menten kinetics and the inhibition mode for PTP1B was determined along with the inhibition constant

Molecular imaging of the bioeffects of β-amyloid and metal ions on live human neuroblastoma cells: internalization, subcellular localization and induction of ROS

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the deposition of extracellular amyloid-β(Aβ) plaques that are rich in metal ions such as zinc, copper and iron. The neurotoxic role of Aβ has been well established but the mechanism of action is still poorly understood. Recent in vitro evidence suggest that Aβ can interact with metal ions such as Zn(II), Cu(II) or Fe(II/III) which promote its aggregation and/or ROS production. However, it is unclear whether this is the case in cells and whether/how extracellular Aβ can get into cells. Our recently developed molecular imaging probes for iron, copper and ROS enable us to look at this in real time in live cells at subcellular resolution. First, we tagged the Aβ covalently with a fluorescent dye which allows its interactions with cells to be monitored under a microscope. Our laser confocal imaging experiments with human neuroblastoma cells revealed that Aβ accumulated at the cell surface first and subsequently entered the cells via endocytosis pathway over a period of a few hours and finally deposited into endosomes/lysosomes in the cells. The deposition of Aβ induced a marked production of oxygen free radicals in the mitochondria of the cells, as revealed by our oxygen free radical probe and colocalization experiments. Incubation of metal ions such as copper(II) increased the production of oxygen radicals significantly while zinc(II) appears to be protective against ROS production. Our data provided compelling and direct evidence on how amyloid-β(Aβ) entering the cells and its induction of oxygen free radicals as well as the effects of metal ions on the radical production at subcellular level

A pH Stable Turn-on Fluorescent Sensor for Imaging Labile Fe3+ in Living Cells

Fluorescent sensors has received considerable interest in recent years because of its ability to provide visualized monitoring of very low concentrations together with the advantages of spatial and temporal resolution. Over the past two decades, several fluorescent sensors for iron (III) have been reported. However, the currently known fluorescent sensors that are capable of cellular iron imaging are largely limited to “turn-off” type, providing useful information but suffering from poor sensitivity, or interference from other metal ions. We have been developing rhodamine based turn-on fluorescent sensors. Here we report a new iron (III) sensor , Rh-PK, which is stable in low pH\u27s and is capable of detecting basal level Fe+3 in the human live cells at subcellular resolution

Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization

Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are distributed across clients, complicating completion tasks due to privacy restrictions. Many frameworks have been proposed to address the issue of federated knowledge graph completion. However, the existing frameworks, including FedE, FedR, and FEKG, have certain limitations. = FedE poses a risk of information leakage, FedR's optimization efficacy diminishes when there is minimal overlap among relations, and FKGE suffers from computational costs and mode collapse issues. To address these issues, we propose a novel method, i.e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion. FLEST decompose the embedding matrix and enables sharing of latent dictionary embeddings to lower privacy risks. Empirical results demonstrate FLEST's effectiveness and efficiency, offering a balanced solution between performance and privacy. FLEST expands the application of federated tensor factorization in KG completion tasks.Comment: Accepted by ICDM 202

Fluorescent Probes for Molecular Imaging of ROS/RNS Species in Living Systems

Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS) are highly reactive species which play crucial roles in many fundamental physiological processes including cellular signalling pathways. Over-production of these reactive species by various stimuli leads to cellular oxidative stress which is linked to various disease conditions. Therefore, the development of novel detection methods for ROS and RNS is of great interest and indispensable for monitoring the dynamic changes of ROS and RNS in cells and for elucidating their mechanisms of trafficking and connections to diseases. We have been recently developing various fluorescent sensors which can selectively detect metal ions, ROS or RNS species in live cells or animals. Our turn-on profluorescent sensors are capable of imaging oxidative stress promoted by metal and H2O2 (i.e. the Fenton Reaction conditions) in living cells (Chem Commun 2010); our highly selective and sensitive iron sensors can image the endogenous exchangeable iron pools and their dynamic changes with subcellular resolution in living neuronal cells (ChemBioChem 2012 and unpublished data), and so do our superoxide sensors (ChemBioChem 2012 and unpublished data). Moreover, we have recently developed nitric oxide (NO) sensors for molecular imaging of stimulated NO production in live cells with subcellular resolution as well as novel near infra red (NIR) sensors for NO imaging in live animals

Coal Rock Breaking Simulation and Cutting Performance Analysis of Disc Cutters

The coal rock breaking ability of disc cutters directly affects the construction efficiency and safety of rescue tunnels in collapsed coal rock formations. This paper establishes the plastic constitutive relationship under the Drucker-Prager (D-P) plasticity criterion, builds up a finite-element analysis (FEA) model for the coal rock breaking with a single cutter on Abaqus FEA, and explores the influence laws of different penetrations and cutting velocities on the rock breaking performance of the cutter. The results show that: as the penetration increased from 3.0 mm to 7.0 mm, the mean vertical force of the cutter grew from 16.97 kN to 23.36 kN, and the mean rolling force rose from 1.79 kN to 3.95 kN. The increase of the cutter\u27s vertical force improves the cutting efficiency, but intensifies the vertical impact, which undermines construction safety. As the cutting velocity increased from 0.6 rad/s to 1.5 rad/s, the mean vertical force grew from 15.64 kN to 22.94 kN, and the mean rolling force rose from 1.46 kN to 4.23 kN. With the increase of cutting velocity, the cutting force grew at an increasing speed. The increase of cutting velocity can improve cutting efficiency, but an excessively fast cutting velocity will weaken the stability of the cutting operation, and add to the wear of the tool. The research method provides theoretical supports to the cutterhead design of tunnel boring machine (TBM) and tunnelling control in broken coal rock formation

Gate-controlled reversible rectifying behaviour in tunnel contacted atomically-thin MoS2_{2} transistor

Atomically-thin 2D semiconducting materials integrated into van der Waals heterostructures have enabled architectures that hold great promise for next generation nanoelectronics. However, challenges still remain to enable their full acceptance as compliant materials for integration in logic devices. Two key-components to master are the barriers at metal/semiconductor interfaces and the mobility of the semiconducting channel, which endow the building-blocks of ${pn}$ diode and field effect transistor. Here, we have devised a reverted stacking technique to intercalate a wrinkle-free h-BN tunnel layer between MoS$_{2}$ channel and contacting electrodes. Vertical tunnelling of electrons therefore makes it possible to suppress the Schottky barriers and Fermi level pinning, leading to homogeneous gate-control of the channel chemical potential across the bandgap edges. The observed unprecedented features of ambipolar ${pn}$ to ${np}$ diode, which can be reversibly gate tuned, paves the way for future logic applications and high performance switches based on atomically thin semiconducting channel.Comment: 23 pages, 5 main figures + 9 SI figure

Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction:A Report From the Huawei Heart Study

BACKGROUND: Current wearable devices enable the detection of atrial fibrillation (AF), but a machine learning (ML)–based approach may facilitate accurate prediction of AF onset. OBJECTIVES: The present study aimed to develop, optimize, and validate an ML-based model for real-time prediction of AF onset in a population at high risk of incident AF. METHODS: A primary ML-based prediction model of AF onset (M1) was developed on the basis of the Huawei Heart Study, a general-population AF screening study using photoplethysmography (PPG)–based smart devices. After optimization in 554 individuals with 469,267 PPG data sets, the optimized ML-based model (M2) was further prospectively validated in 50 individuals with paroxysmal AF at high risk of AF onset, and compared with 72-hour Holter electrocardiographic (ECG) monitoring, a criterion standard, from September 1, 2019, to November 5, 2019. RESULTS: Among 50 patients with paroxysmal AF (mean age 67 ± 12 years, 40% women), there were 2,808 AF events from a total of 14,847,356 ECGs over 72 hours and 6,860 PPGs (45.83 ± 13.9 per subject per day). The best performance of M1 for AF onset prediction was achieved 4 hours before AF onset (area under the receiver operating characteristic curve: 0.94; 95% confidence interval: 0.93-0.94). M2 sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (at 0 to 4 hours before AF onset) were 81.9%, 96.6%, 96.4%, 83.1%, and 88.9%, respectively, compared with 72-hour Holter ECG. CONCLUSIONS: The PPG- based ML model demonstrated good ability for AF prediction in advance. (Mobile Health [mHealth] technology for improved screening, patient involvement and optimizing integrated care in atrial fibrillation; ChiCTR-OOC-17014138