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"Wu, Hao"
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Immunomodulatory function and anti-tumor mechanism of natural polysaccharides: A review
Polysaccharides extracted from natural resources have attracted extensive attention in biomedical research and pharmaceutical fields, due to their medical values in anti-tumor, immunomodulation, drug delivery, and many other aspects. At present, a variety of natural polysaccharides have been developed as adjuvant drugs in clinical application. Benefit from their structural variability, polysaccharides have great potential in regulating cellular signals. Some polysaccharides exert direct anti-tumor effects by inducing cell cycle arrest and apoptosis, while the majority of polysaccharides can regulate the host immune system and indirectly inhibit tumors by activating either non-specific or specific immune responses. As the essential of microenvironment in the process of tumor development has been gradually revealed, some polysaccharides were found to inhibit the proliferation and metastasis of tumor cells via tumoral niche modulation. Here, we focused on natural polysaccharides with biomedical application potential, reviewed the recent advancement in their immunomodulation function and highlighted the importance of their signaling transduction feature for the antitumor drug development.
Journal Article
EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers
Background
A critical and challenging process in immunotherapy is to identify cancer patients who could benefit from immune checkpoint inhibitors (ICIs). Exploration of predictive biomarkers could help to maximize the clinical benefits. Eph receptors have been shown to play essential roles in tumor immunity. However, the association between EPH gene mutation and ICI response is lacking.
Methods
Clinical data and whole-exome sequencing (WES) data from published studies were collected and consolidated as a discovery cohort to analyze the association between EPH gene mutation and efficacy of ICI therapy. Another independent cohort from Memorial Sloan Kettering Cancer Center (MSKCC) was adopted to validate our findings. The Cancer Genome Atlas (TCGA) cohort was used to perform anti-tumor immunity and pathway enrichment analysis.
Results
Among fourteen EPH genes, EPHA7-mutant (EPHA7-MUT) was enriched in patients responding to ICI therapy (FDR adjusted
P
< 0.05). In the discovery cohort (
n
= 386), significant differences were detected between EPHA7-MUT and EPHA7-wildtype (EPHA7-WT) patients regarding objective response rate (ORR, 52.6% vs 29.1%, FDR adjusted
P
= 0.0357) and durable clinical benefit (DCB, 70.3% vs 42.7%, FDR adjusted
P
= 0.0200). In the validation cohort (
n
= 1144), significant overall survival advantage was observed in EPHA7-MUT patients (HR = 0.62 [95% confidence interval, 0.39 to 0.97], multivariable adjusted
P
= 0.0367), which was independent of tumor mutational burden (TMB) and copy number alteration (CNA). Notably, EPHA7-MUT patients without ICI therapy had significantly worse overall survival in TCGA cohort (HR = 1.33 [95% confidence interval, 1.06 to 1.67], multivariable adjusted
P
= 0.0139). Further gene set enrichment analysis revealed enhanced anti-tumor immunity in EPHA7-MUT tumor.
Conclusions
EPHA7-MUT successfully predicted better clinical outcomes in ICI-treated patients across multiple cancer types, indicating that EPHA7-MUT could serve as a potential predictive biomarker for immune checkpoint inhibitors.
Journal Article
Identification of Confirmatory Factor Analysis Models of Different Levels of Invariance for Ordered Categorical Outcomes
This article considers the identification conditions of confirmatory factor analysis (CFA) models for ordered categorical outcomes with invariance of different types of parameters across groups. The current practice of invariance testing is to first identify a model with only configural invariance and then test the invariance of parameters based on this identified baseline model. This approach is not optimal because different identification conditions on this baseline model identify the scales of latent continuous responses in different ways. Once an invariance condition is imposed on a parameter, these identification conditions may become restrictions and define statistically non-equivalent models, leading to different conclusions. By analyzing the transformation that leaves the model-implied probabilities of response patterns unchanged, we give identification conditions for models with invariance of different types of parameters without referring to a specific parametrization of the baseline model. Tests based on this approach have the advantage that they do not depend on the specific identification condition chosen for the baseline model.
Journal Article
Diboron compound-based organic light-emitting diodes with high efficiency and reduced efficiency roll-off
Organic light-emitting diodes (OLEDs) based on thermally activated delayed fluorescence (TADF) materials are promising for the realization of highly efficient light emitters. However, such devices have so far suffered from efficiency roll-off at high luminance. Here, we report the design and synthesis of two diboron-based molecules, CzDBA and tBuCzDBA, which show excellent TADF properties and yield efficient OLEDs with very low efficiency roll-off. These donor–acceptor–donor (D–A–D) type and rod-like compounds concurrently generate TADF with a photoluminescence quantum yield of ~100% and an 84% horizontal dipole ratio in the thin film. A green OLED based on CzDBA exhibits a high external quantum efficiency of 37.8 ± 0.6%, a current efficiency of 139.6 ± 2.8 cd A−1 and a power efficiency of 121.6 ± 3.1 lm W−1 with an efficiency roll-off of only 0.3% at 1,000 cd m−2. The device has a peak emission wavelength of 528 nm and colour coordinates of the Commission International de l´Eclairage (CIE) of (0.31, 0.61), making it attractive for colour-display applications.
Journal Article
Does Hawking effect always degrade fidelity of quantum teleportation in Schwarzschild spacetime?
A
bstract
Previous studies have shown that the Hawking effect always destroys quantum correlations and the fidelity of quantum teleportation in the Schwarzschild black hole. Here, we investigate the fidelity of quantum teleportation of Dirac fields between users in Schwarzschild spacetime. We find that, with the increase of the Hawking temperature, the fidelity of quantum teleportation can monotonically increase, monotonically decrease, or non-monotonically increase, depending on the choice of the initial state, which means that the Hawking effect can create net fidelity of quantum teleportation. This striking result banishes the extended belief that the Hawking effect of the black hole can only destroy the fidelity of quantum teleportation. We also find that quantum steering cannot fully guarantee the fidelity of quantum teleportation in Schwarzschild spacetime. This new unexpected source may provide a new idea for the experimental evidence of the Hawking effect.
Journal Article
Achieving ultrahigh instantaneous power density of 10 MW/m2 by leveraging the opposite-charge-enhanced transistor-like triboelectric nanogenerator (OCT-TENG)
Converting various types of ambient mechanical energy into electricity, triboelectric nanogenerator (TENG) has attracted worldwide attention. Despite its ability to reach high open-circuit voltage up to thousands of volts, the power output of TENG is usually meager due to the high output impedance and low charge transfer. Here, leveraging the opposite-charge-enhancement effect and the transistor-like device design, we circumvent these limitations and develop a TENG that is capable of delivering instantaneous power density over 10 MW/m
2
at a low frequency of ~ 1 Hz, far beyond that of the previous reports. With such high-power output, 180 W commercial lamps can be lighted by a TENG device. A vehicle bulb containing LEDs rated 30 W is also wirelessly powered and able to illuminate objects further than 0.9 meters away. Our results not only set a record of the high-power output of TENG but also pave the avenues for using TENG to power the broad practical electrical appliances.
TENG suffers from two fundamental limitations: high output impedance and low charge transfer. Herein, these limitations are circumvented by leveraging the opposite-charge-enhancement effect and transistor-like device design, thereby achieving the instantaneous power density over 10 MW/m
2
at the low frequency of ~ 1 Hz.
Journal Article
VAMPnets for deep learning of molecular kinetics
There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.
Extracting kinetic models from high-throughput molecular dynamics (MD) simulations is laborious and prone to human error. Here the authors introduce a deep learning framework that automates construction of Markov state models from MD simulation data.
Journal Article
TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis
In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the “reference-free deconvolution” methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at
https://bioconductor.org/packages/TOAST
.
Journal Article
Variational Approach for Learning Markov Processes from Time Series Data
Inference, prediction, and control of complex dynamical systems from time series is important in many areas, including financial markets, power grid management, climate and weather modeling, or molecular dynamics. The analysis of such highly nonlinear dynamical systems is facilitated by the fact that we can often find a (generally nonlinear) transformation of the system coordinates to features in which the dynamics can be excellently approximated by a linear Markovian model. Moreover, the large number of system variables often change collectively on large time- and length-scales, facilitating a low-dimensional analysis in feature space. In this paper, we introduce a variational approach for Markov processes (VAMP) that allows us to find optimal feature mappings and optimal Markovian models of the dynamics from given time series data. The key insight is that the best linear model can be obtained from the top singular components of the Koopman operator. This leads to the definition of a family of score functions called VAMP-
r
which can be calculated from data, and can be employed to optimize a Markovian model. In addition, based on the relationship between the variational scores and approximation errors of Koopman operators, we propose a new VAMP-E score, which can be applied to cross-validation for hyper-parameter optimization and model selection in VAMP. VAMP is valid for both reversible and nonreversible processes and for stationary and nonstationary processes or realizations.
Journal Article