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Author: Guo, Yachong × Type: Text ×

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    Ultrathin 2D Titanium Carbide MXene (Ti3C2Tx) Nanoflakes Activate WNT/HIF-1α-Mediated Metabolism Reprogramming for Periodontal Regeneration
    (Weinheim : Wiley-VCH, 2021) Cui, Di; Kong, Na; Ding, Liang; Guo, Yachong; Yang, Wenrong; Yan, Fuhua
    Periodontal defect regeneration in severe periodontitis relies on the differentiation and proliferation of periodontal ligament cells (PDLCs). Recently, an emerging 2D nanomaterial, MXene (Ti3C2Tx), has gained more and more attention due to the extensive antibacterial and anticancer activity, while its potential biomedical application on tissue regeneration remains unclear. Through a combination of experimental and multiscale simulation schemes, Ti3C2Tx has exhibited satisfactory biocompatibility and induced distinguish osteogenic differentiation of human PDLCs (hPDLCs), with upregulated osteogenesis-related genes. Ti3C2Tx manages to activate the Wnt/β-catenin signaling pathway by enhancing the Wnt-Frizzled complex binding, thus stabilizing HIF-1α and altering metabolic reprogramming into glycolysis. In vivo, hPDLCs pretreated by Ti3C2Tx display excellent performance in new bone formation and osteoclast inhibition with enhanced RUNX2, HIF-1α, and β-catenin in an experimental rat model of periodontal fenestration defects, indicating that this material has high efficiency of periodontal regeneration promotion. It is demonstrated in this work that Ti3C2Tx has highly efficient therapeutic effects in osteogenic differentiation and periodontal defect repairment. © 2021 The Authors. Advanced Healthcare Materials published by Wiley-VCH GmbH
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    Neural network learns physical rules for copolymer translocation through amphiphilic barriers
    (London : Nature Publ. Group, 2020) Werner, Marco; Guo, Yachong; Baulin, Vladimir A.
    Recent developments in computer processing power lead to new paradigms of how problems in many-body physics and especially polymer physics can be addressed. Parallel processors can be exploited to generate millions of molecular configurations in complex environments at a second, and concomitant free-energy landscapes can be estimated. Databases that are complete in terms of polymer sequences and architecture form a powerful training basis for cross-checking and verifying machine learning-based models. We employ an exhaustive enumeration of polymer sequence space to benchmark the prediction made by a neural network. In our example, we consider the translocation time of a copolymer through a lipid membrane as a function of its sequence of hydrophilic and hydrophobic units. First, we demonstrate that massively parallel Rosenbluth sampling for all possible sequences of a polymer allows for meaningful dynamic interpretation in terms of the mean first escape times through the membrane. Second, we train a multi-layer neural network on logarithmic translocation times and show by the reduction of the training set to a narrow window of translocation times that the neural network develops an internal representation of the physical rules for sequence-controlled diffusion barriers. Based on the narrow training set, the network result approximates the order of magnitude of translocation times in a window that is several orders of magnitude wider than the training window. We investigate how prediction accuracy depends on the distance of unexplored sequences from the training window. © 2020, The Author(s).
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    Serum lactate dehydrogenase activities as systems biomarkers for 48 types of human diseases
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Wu, Yuling; Lu, Caixia; Pan, Nana; Zhang, Meng; An, Yi; Xu, Mengyuan; Zhang, Lijuan; Guo, Yachong; Tan, Lijuan
    Most human diseases are systems diseases, and systems biomarkers are better fitted for diagnostic, prognostic, and treatment monitoring purposes. To search for systems biomarker candidates, lactate dehydrogenase (LDH), a housekeeping protein expressed in all living cells, was investigated. To this end, we analyzed the serum LDH activities from 172,933 patients with 48 clinically defined diseases and 9528 healthy individuals. Based on the median values, we found that 46 out of 48 diseases, leading by acute myocardial infarction, had significantly increased (p < 0.001), whereas gout and cerebral ischemia had significantly decreased (p < 0.001) serum LDH activities compared to the healthy control. Remarkably, hepatic encephalopathy and lung fibrosis had the highest AUCs (0.89, 0.80), sensitivities (0.73, 0.56), and specificities (0.90, 0.91) among 48 human diseases. Statistical analysis revealed that over-downregulation of serum LDH activities was associated with blood-related cancers and diseases. LDH activities were potential systems biomarker candidates (AUCs > 0.8) for hepatic encephalopathy and lung fibrosis.