Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

Xmrk, kras and myc transgenic zebrafish liver cancer models share molecular signatures with subsets of human hepatocellular carcinoma.

Abstract: Previously three oncogene transgenic zebrafish lines with inducible expression of xmrk, kras or Myc in the liver have been generated and these transgenic lines develop oncogene-addicted liver tumors upon chemical induction. In the current study, comparative transcriptomic approaches were used to examine the correlation of the three induced transgenic liver cancers with human liver cancers. RNA profiles from the three zebrafish tumors indicated relatively small overlaps of significantly deregulated genes and biological pathways. Nevertheless, the three transgenic tumor signatures all showed significant correlation with advanced or very advanced human hepatocellular carcinoma (HCC). Interestingly, molecular signature from each oncogene-induced zebrafish liver tumor correlated with only a small subset of human HCC samples (24–29%) and there were conserved up-regulated pathways between the zebrafish and correlated human HCC subgroup. The three zebrafish liver cancer models together represented nearly half (47.2%) of human HCCs while some human HCCs showed significant correlation with more than one signature defined from the three oncogene-addicted zebrafish tumors. In contrast, commonly deregulated genes (21 up and 16 down) in the three zebrafish tumor models generally showed accordant deregulation in the majority of human HCCs, suggesting that these genes might be more consistently deregulated in a broad range of human HCCs with different molecular mechanisms and thus serve as common diagnosis markers and therapeutic targets. Thus, these transgenic zebrafish models with well-defined oncogene-induced tumors are valuable tools for molecular classification of human HCCs and for understanding of molecular drivers in hepatocarcinogenesis in each human HCC subgroup.

Construction of high strength hollow fibers by self-assembly of a stiff polysaccharide with short branches in water

Abstract: The development of biological high-performance materials fabricated from natural polysaccharides has attracted great attention for a sustainable world. In this work, hollow fibers with high strength were spun from a polysaccharide aqueous solution at a concentration of 0.02 g mL−1. The polysaccharide was a comb-like β-glucan with short branches isolated from Auricularia auricula-judae, coded as AF1. Atomic force microscopy (AFM) and transmission electron microscopy (TEM) confirmed directly that AF1 existed as a stiff chain conformation in water, and displayed parallel self-orientation behavior. AF1 could self-assemble into well defined hollow nanofibers with diameters less than 100 nm and lengths of tens of micrometers in dilute solution, supported by scanning electron microscopy (SEM). Moreover, AF1 in the disulfonated tetraphenylethene (TPE-SO3Na) aqueous solution exhibited strong luminescence, indicating that the TPE-SO3Na molecules without luminescence in water were trapped in the cavities of the hollow nanofibers through hydrophobic interactions, leading to the aggregation-induced emission (AIE). The nanofibers were composed of relatively hydrophobic inner-walls and hydrophilic shells in water. Interestingly, SEM and polarized light microscopy verified that the nanofibers fused to form an ordered architecture of lamella and then tended to curl into hollow fibers in relatively concentrated solution. The hollow fibers exhibited excellent tensile strength, biocompatibility, organic solvent resistance and birefringence. A schematic model was proposed to describe the construction of the hollow fibers via the hierarchical self-assembly process. The new materials would have potential applications such as drug release as a new class of fibrous carrier, indicators with fluorescence to detect cell growth in cell transplantation, and biomolecular recognition (e.g., DNA).

Functionalization of small black phosphorus nanoparticles for targeted imaging and photothermal therapy of cancer

Abstract: Black phosphorus (BP) nanomaterials have attracted extensive attention due to their unique physical, chemical, and biological properties. In this study, small BP nanoparticles were synthesized and modified with dextran and poly(ethyleneimine) for functionalization with folic acid and cyanine 7. The functionalized BP nanoparticles exhibit excellent biocompatibility, stability, and near infrared optical properties for targeted imaging of tumors through photoacoustic imaging and near-infrared fluorescence imaging. They also display high photothermal conversion efficiency for photothermal therapy of cancer. This work demonstrates the potential of functionalized small BP nanoparticles as an emerging nanotheranostic agent for the diagnosis and treatment of cancer.

Maternal Benzene Exposure during Pregnancy and Risk of Childhood Acute Lymphoblastic Leukemia: A Meta-Analysis of Epidemiologic Studies

Abstract: Background The prevalence of childhood leukemia is increasing rapidly all over the world. However, studies on maternal benzene exposure during pregnancy and childhood acute lymphoblastic leukemia (ALL) have not been systematically assessed. Therefore, we performed a meta-analysis to investigate the association between maternal solvent, paint, petroleum exposure, and smoking during pregnancy and risk of childhood ALL. Methods Relevant studies up to September 1st, 2013 were identified by searching the PubMed, EMBASE, Cochrane library and the Web of Science databases. The effects were pooled using either fixed or random effect models based on the heterogeneity of the studies. Results Twenty-eight case-control studies and one cohort study were included for analysis, with a total of 16,695 cases and 1,472,786 controls involved. Pooled odds ratio (OR) with 95% confidence interval (CI) for ALL was 1.25 (1.09, 1.45) for solvent, 1.23 (1.02, 1.47) for paint, 1.42 (1.10, 1.84) for petroleum exposure, and 0.99 (0.93, 1.06) for maternal smoking during pregnancy. No publication bias was found in this meta-analysis and consistent results were observed for subgroup and sensitivity analyses. Conclusions Childhood ALL was associated with maternal solvent, paint, and petroleum exposure during pregnancy. No association was found between ALL and maternal smoking during pregnancy. Avoidance of maternal occupational and environmental benzene exposure during pregnancy could contribute to a decrease in the risk of childhood ALL.

Aqueous synthesis of PEGylated copper sulfide nanoparticles for photoacoustic imaging of tumors

Abstract: By integrating high imaging sensitivity and high resolution in a single modality, photoacoustic (PA) imaging emerges as a promising diagnostic tool for clinical applications. Benefiting from the absorption in the near-infrared region (NIR), copper sulfide nanoparticles (NPs) as a contrast agent are potentially useful for increasing the sensitivity of PA imaging. However, the aqueous synthesis of size-tunable, biocompatible and colloidally stable copper sulfide NPs remains challenging due to the intrinsic dipole-dipole interactions among particles. In this work, aqueous synthesis of PEGylated copper sulfide NPs with controllable size between 3 and 7 nm was developed. The particle size-dependent contrast enhancement effect of the copper sulfide NPs for PA imaging was carefully studied both in vitro and in vivo. Although the contrast enhancement effect of the copper sulfide NPs is proportional to particle size, the in vivo studies revealed that copper sulfide NPs smaller than 5 nm presented higher tumor imaging performance, especially at the tumor boundary site, which was further discussed in combination with the pharmacokinetic behaviors of differently sized particles.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Highly accurate protein structure prediction with AlphaFold

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.