Publications by authors named "Ziyin Wu"

Article Synopsis
  • The study focuses on the protein p53, which plays a key role in tumor suppression and is linked to the frequently mutated TP53 gene in many cancers, highlighting its multiple functions in regulating various cellular processes related to cancer development.
  • The paper aims to provide a comprehensive summary of p53's roles in the tumor microenvironment (TME), mA modification, and numerous forms of programmed cell death (PCD), which are crucial for cancer progression and metastasis.
  • It discusses important interactions between cancer immunity, cell death mechanisms, and RNA modifications, while outlining the complex roles of both wild-type and mutant p53, ultimately aiming to inform the development of new cancer therapies.
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The near-trench coseismic rupture behaviour of the 2011 Tohoku-Oki earthquake remains poorly understood due to the scarcity of near-field observations. Differential bathymetry offers a unique approach to studying offshore coseismic seafloor deformation but has a limited horizontal resolution. Here we use differential bathymetry estimates with improved horizontal resolutions to investigate near-trench coseismic slip behaviours in the 2011 Tohoku-Oki earthquake.

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Article Synopsis
  • Current therapies for ischemic stroke are limited, but ginkgolides, particularly through a compound called diterpene ginkgolides meglumine injection (DGMI), show potential for neurological improvement in stroke patients despite the lack of extensive evidence.
  • A study using RNA sequencing identified ginkgolides' potential antiplatelet and neuroprotective effects, with the strongest response observed in certain cell models, suggesting their beneficial role in stroke-related conditions.
  • Further experiments demonstrated that ginkgolides (specifically GB and GK) protect against demyelination and promote regeneration of nerve cells, highlighting their promise for developing therapies that support remyelination and mitigate stroke progression.
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The seafloor topography estimation is very important, while the bathymetry data and gravity data are scarce and uneven, which results in large errors in the inversion of the seafloor topography. In this paper, in order to reduce the influence of errors and improve the accuracy of seafloor inversion, the influence of different resolution data on the inversion topography in the Emperor Seamount Chain are investigated by combining ship water depth data and satellite gravity anomaly data released by SIO V29.1.

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Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes.

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Ethnopharmacological Relevance: Cardiomyopathy is a common cause of heart failure and may lead to increased risk of sudden cardiac death, lacking simple, safe and effective treatment strategies due to unclear pathogenesis. Ginkgo biloba L. leaves (GBLs), a traditional Chinese medicine (TCM), has been widely used in clinical medicine for improving blood circulation, and was demonstrated to be effective on cardiomyopathy in preclinical studies.

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Over the past 60 years, because of the combined impacts of human activities and climate change, the sediment load of the nine major rivers (the Yellow, Yangtze, Pearl, Songhuajiang, Liaohe, Haihe, Huaihe, Qiantangjiang, and Minjiang rivers) in China has dropped by 85%, which had caused serious environmental problems such as reservoir siltation and estuary erosion. However, quantitatively evaluating the impact of different human activities on this decline is still an unsolved and complex problem. Based on a big new data set from 27 gauge stations and 469 meteorological stations, we established five methods to assess sediment loss of China's nine major rivers.

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Temporal lobe epilepsy (TLE) is the most prevalent and often devastating form of epilepsy. The molecular mechanism underlying the development of TLE remains largely unclear, which hinders the discovery of effective antiepileptogenic drugs. Here we adopted a systems-level approach integrating transcriptomic profiles of three epileptogenesis stages to identify key regulators underlying epilepsy progression.

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Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns.

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To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method.

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After publication of the article [1], it has been brought to our attention that an author's name was spelt incorrectly in the original published article. Yonghua Wang was previously spelt "Yonghua Wan". This has now been corrected in the revised version of the article.

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Background: Dengue virus (DENV) is an increasing global health threat and associated with induction of both a long-lived protective immune response and immune-suppression. So far, the potency of treatment of DENV via antiviral drugs is still under investigation. Recently, increasing evidences suggest the potential role of microRNAs (miRNAs) in regulating DENV.

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Neuroinflammation is characterized by the elaborated inflammatory response repertoire of central nervous system tissue. The limitations of the current treatments for neuroinflammation are well-known side effects in the clinical trials of monotherapy. Drug combination therapies are promising strategies to overcome the compensatory mechanisms and off-target effects.

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Multi-herb therapy has been widely used in Traditional Chinese medicine and tailored to meet the specific needs of each individual. However, the potential molecular or systems mechanisms of them to treat various diseases have not been fully elucidated. To address this question, a systems pharmacology approach, integrating pharmacokinetics, pharmacology and systems biology, is used to comprehensively identify the drug-target and drug-disease networks, exemplified by three representative Radix Salviae Miltiorrhizae herb pairs for treating various diseases (coronary heart disease, dysmenorrheal and nephrotic syndrome).

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Though cardiovascular diseases (CVDs) and gastrointestinal disorders (GIDs) are different diseases associated with different organs, they are highly correlated clinically. Importantly, in Traditional Chinese Medicine (TCM), similar treatment strategies have been applied in both diseases. However, the etiological mechanisms underlying them remain unclear.

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Due to the large direct and indirect productivity losses in the livestock industry caused by bovine viral diarrhea (BVD) and the lack of effective pharmacological therapies, developing an efficient treatment is extremely urgent. Traditional Chinese medicines (TCMs) that simultaneously address multiple targets have been proven to be effective therapies for BVD. However, the potential molecular action mechanisms of TCMs have not yet been systematically explored.

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Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them.

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The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented.

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A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale.

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Holistic medicine is an interdisciplinary field of study that integrates all types of biological information (protein, small molecules, tissues, organs, external environmental signals, etc.) to lead to predictive and actionable models for health care and disease treatment. Despite the global and integrative character of this discipline, a comprehensive picture of holistic medicine for the treatment of complex diseases is still lacking.

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Motivation: Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered.

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