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"Ming Chen"
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Collective variable-based enhanced sampling and machine learning
Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations.
Graphic abstract
Journal Article
Vasculogenic mimicry formation in EBV-associated epithelial malignancies
Epstein-Barr virus (EBV)-associated epithelial cancers, including nasopharyngeal carcinoma (NPC) and approximately 10% of gastric cancers, termed EBVaGC, represent 80% of all EBV-related malignancies. However, the exact role of EBV in epithelial cancers remains elusive. Here, we report that EBV functions in vasculogenic mimicry (VM). Epithelial cancer cells infected with EBV develop tumor vascular networks that correlate with tumor growth, which is different from endothelial-derived angiogenic vessels and is VEGF-independent. Mechanistically, activation of the PI3K/AKT/mTOR/HIF-1α signaling cascade, which is partly mediated by LMP2A, is responsible for EBV-induced VM formation. Both xenografts and clinical samples of NPC and EBVaGC exhibit VM histologically, which are correlated with AKT and HIF-1α activation. Furthermore, although anti-VEGF monotherapy shows limited effects, potent synergistic antitumor activities are achieved by combination therapy with VEGF and HIF-1α-targeted agents. Our findings suggest that EBV creates plasticity in epithelial cells to express endothelial phenotype and provides a novel EBV-targeted antitumor strategy.
Journal Article
Safety and Tolerability of Intra-Articular Injection of Adipose-Derived Mesenchymal Stem Cells GXCPC1 in 11 Subjects With Knee Osteoarthritis: A Nonrandomized Pilot Study Without a Control Arm
The current study aimed to determine the safety profile of intra-articular-injected allogeneic adipose-derived mesenchymal stem cells (ADSCs) GXCPC1 in subjects with knee osteoarthritis (OA) and its preliminary efficacy outcome. The 3 + 3 phase I study was designed with two dose-escalation cohorts: low dose (6.7 × 106 GXCPC1, N = 5) and high dose (4 × 107 GXCPC1, N = 6). The primary endpoint was safety, which was evaluated by recording adverse events throughout the trial; the secondary endpoints included total, pain, stiffness, and function subscales of the Western Ontario and McMaster Universities Arthritis Index (WOMAC), Visual Analogue Scale (VAS) for pain, and 12-Item Short Form (SF-12) health survey questionnaire. The GXCPC1 treatment was found to be safe after 1 year of follow-up with no treatment-related severe adverse events observed. When compared to baseline, subjects in both the low- and high-dose cohorts demonstrated improving trends in pain and knee function after receiving GXCPC1 treatment. Generally, the net change in pain (95% confidence interval (CI) = −7.773 to −2.561t at 12 weeks compared to baseline) and knee function (95% CI = −24.297 to −10.036t at 12 weeks compared to baseline) was better in subjects receiving high-dose GXCPC1. Although this study included a limited number of subjects without a placebo arm, it showed that the intra-articular injection of ADSCs was safe and well-tolerated in subjects with therapeutic alternatives to treat knee OA. However, a larger scale study with an appropriate control would be necessary for clinical efficacy in the following study.
Journal Article
Photocatalytic decarboxylative alkylations mediated by triphenylphosphine and sodium iodide
Most photoredox catalysts in current use are precious metal complexes or synthetically elaborate organic dyes, the cost of which can impede their application for large-scale industrial processes. We found that a combination of triphenylphosphine and sodium iodide under 456-nanometer irradiation by blue light–emitting diodes can catalyze the alkylation of silyl enol ethers by decarboxylative coupling with redox-active esters in the absence of transition metals. Deaminative alkylation using Katritzky’s N-alkylpyridinium salts and trifluoromethylation using Togni’s reagent are also demonstrated. Moreover, the phosphine/iodide-based photoredox system catalyzes Minisci-type alkylation of N-heterocycles and can operate in tandem with chiral phosphoric acids to achieve high enantioselectivity in this reaction.
Journal Article
Data transformation and recombination based on large model: design of multi-group personalized Bridge models
Bridge life cycle management is a collaborative work process involving multiple groups, and finite element analysis is a popular method used in bridge management. The frequent establishment of finite element models by diverse groups directly reduces the efficiency and quality of bridge management. To address this issue, a large model of bridge model transformation was developed by integrating domain knowledge based on existing general large models, and data transformation and data recombination were proposed. Simulation investigation demonstrates that the proposed bridge modeling approach may generate finite element models that fulfill the various needs of participating organizations based on their specific criteria. The domain knowledge base supports the data transformation algorithm, which calls the Pangu Large Model’s scientific computing model to enable autonomous data translation, minimizing the amount of time spent manually participating in the modeling process. The seismic study of the bridge reveals that the finite element model generated using data recombination differs by 5.93% from the analytical results of the manually established finite element model. Overall, data transformation and recombination modeling methods have greatly increased bridge modeling efficiency, reducing the need for duplicate modeling and improving bridge management efficiency.
Journal Article
A Tppp3+Pdgfra+ tendon stem cell population contributes to regeneration and reveals a shared role for PDGF signalling in regeneration and fibrosis
Tendon injuries cause prolonged disability and never recover completely. Current mechanistic understanding of tendon regeneration is limited. Here, we use single-cell transcriptomics to identify a tubulin polymerization-promoting protein family member 3-expressing (
Tppp3
+
) cell population as potential tendon stem cells. Through inducible lineage tracing, we demonstrate that these cells can generate new tenocytes and self-renew upon injury. A fraction of
Tppp3
+
cells expresses platelet-derived growth factor receptor alpha (
Pdfgra
). Ectopic platelet-derived growth factor-AA (PDGF-AA) protein induces new tenocyte production while inactivation of
Pdgfra
in
Tppp3
+
cells blocks tendon regeneration. These results support
Tppp3
+
Pdgfra
+
cells as tendon stem cells. Unexpectedly,
Tppp3
−
Pdgfra
+
fibro-adipogenic progenitors coexist in the tendon stem cell niche and give rise to fibrotic cells, revealing a clandestine origin of fibrotic scars in healing tendons. Our results explain why fibrosis occurs in injured tendons and present clinical challenges to enhance tendon regeneration without a concurrent increase in fibrosis by PDGF application.
Using single-cell transcriptomics and in vivo injury models, Harvey et al. identify a
Tppp3
+
Pdgfra
+
stem cell population in the tendon sheath and demonstrate the role of PDGFRα signalling in regeneration and fibrosis.
Journal Article